diff --git a/docs/docs/Feed-Forward-Guide.md b/docs/docs/Feed-Forward-Guide.md
index 96ba0fbf60da..aafb30ee983c 100644
--- a/docs/docs/Feed-Forward-Guide.md
+++ b/docs/docs/Feed-Forward-Guide.md
@@ -36,7 +36,7 @@ If you wish to use the above properties, then you can configure them for the fir
 
 # Feed Forward Properties
 
-Components that support feed forward have two algorithm properties that control the feed forward behavior: `FEED_FORWARD_TYPE` and `FEED_FORWARD_TOP_CONFIDENCE_COUNT`.
+Components that support feed forward have two algorithm properties that control the feed forward behavior: `FEED_FORWARD_TYPE` and `FEED_FORWARD_TOP_QUALITY_COUNT`.
 
 `FEED_FORWARD_TYPE` can be set to the following values:
 
@@ -47,9 +47,9 @@ Components that support feed forward have two algorithm properties that control
 
 > **NOTE:** When using `REGION`, the location of the region within the frame, and the size of the region, may be different for each detection in the feed forward track. Thus, `REGION` should not be used by algorithms that perform region tracking and require a consistent coordinate space from detection to detection. For those algorithms, use `SUPERSET_REGION` instead. That will ensure that each detection region is relative to the upper right corner of the superset region for that track.
 
-`FEED_FORWARD_TOP_CONFIDENCE_COUNT` allows you to drop low confidence detections from feed forward tracks. Setting the property to a value less than or equal to 0 has no effect. In that case all detections in the feed forward track will be processed.
+`FEED_FORWARD_TOP_QUALITY_COUNT` allows you to drop low quality detections from feed forward tracks. Setting the property to a value less than or equal to 0 has no effect. In that case all detections in the feed forward track will be processed.
 
-When `FEED_FORWARD_TOP_CONFIDENCE_COUNT` is set to a number greater than 0, say 5, then the top 5 detections in the feed forward track (based on highest confidence) will be processed. If the track contains less than 5 detections then all of the detections in the track will be processed. If one or more detections have the same confidence value, then the detection(s) with the lower frame index take precedence.
+When `FEED_FORWARD_TOP_QUALITY_COUNT` is set to a number greater than 0, say 5, then the top 5 highest quality detections in the feed forward track will be processed. Determination of quality is based on the job property `QUALITY_SELECTION_PROPERTY`, which defaults to `CONFIDENCE`, but may be set to a different detection property. [See the documentation in the "Quality Selection Guide"] If the track contains less than 5 detections then all of the detections in the track will be processed. If one or more detections have the same quality value, then the detection(s) with the lower frame index take precedence.
 
 
 # Superset Region
@@ -166,7 +166,7 @@ Refer to `runMogThenCaffeFeedForwardExactRegionTest()` in the [`TestSystemOnDiff
 
 > **NOTE:** Short and/or spurious MOG motion tracks will result in more overhead work when performing feed forward. To mitigate this, consider setting the `MERGE_TRACKS`, `MIN_GAP_BETWEEN_TRACKS`, and `MIN_TRACK_LENGTH` properties to generate longer motion tracks and discard short and/or spurious motion tracks.
 
-> **NOTE:** It doesn’t make sense to use `FEED_FORWARD_TOP_CONFIDENCE_COUNT` on a pipeline stage that follows a MOG or SuBSENSE motion detection stage. That’s because those motion detectors don’t generate tracks with confidence values. Instead, `FEED_FORWARD_TOP_CONFIDENCE_COUNT` could potentially be used when feeding person tracks into a face detector, for example, if those person tracks have confidence values.
+> **NOTE:** It doesn’t make sense to use `FEED_FORWARD_TOP_QUALITY_COUNT` on a pipeline stage that follows a MOG or SuBSENSE motion detection stage. That’s because those motion detectors don’t generate tracks with confidence values (`CONFIDENCE`being the default value for the `QUALITY_SELECTION_PROPERTY` job property). Instead, `FEED_FORWARD_TOP_QUALITY_COUNT` could potentially be used when feeding person tracks into a face detector, for example, if the detections in those person tracks have the requested `QUALITY_SELECTION_PROPERTY` set.
 
 
 <b>OCV Face Detection with MOG Motion Detection and Feed Forward Superset Region</b>
diff --git a/docs/docs/Quality-Selection-Guide.md b/docs/docs/Quality-Selection-Guide.md
new file mode 100644
index 000000000000..979270400640
--- /dev/null
+++ b/docs/docs/Quality-Selection-Guide.md
@@ -0,0 +1,34 @@
+**NOTICE:** This software (or technical data) was produced for the U.S. Government under contract, and is subject to the
+Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.
+
+# Introduction
+
+There are a few places in OpenMPF where the quality of a detection comes into play. Here, "detection quality" is defined to be a measurement of how "good" the detection is that can be used to rank the detections in a track from highest to lowest quality. Traditionally, OpenMPF has used detection "confidence" as an indicator of quality. Detection confidence is assumed to be greater than 0, and higher values indicate higher quality detections. However, there are some components that do not compute a confidence value for its detections, and there may be others that compute a different value that is a better measure of quality for that detection algorithm.
+
+The primary way in which the OpenMPF system uses detection quality is to determine the track "exemplar", which is the highest quality detection in the track.  For components that do not compute a confidence value, or where all detections have identical confidence, the workflow manager would choose the first detection in the track as the exemplar. For components that would rather use a different property as an indicator of quality, there was no means of indicating that to the workflow manager.
+
+The workflow manager also filters detections and tracks by quality, so that only high quality detections and tracks appear in the final output. This filtering can be controlled by the user through the `CONFIDENCE_THRESHOLD` property. All detections below this threshold are discarded, and if all the detections in a track are discarded, then the track itself is also discarded. Note that components may do this filtering themselves, while others leave it to the workflow manager to do the filtering. The thresholding process can also be circumvented by setting this threshold to a value less than the lowest possible value for the confidence. For example, if the detection confidence computed by a component has values in the range 0 to 1, then setting the threshold property to -1 will result in all detections and all tracks being retained.
+
+The workflow manager also uses confidence to select the detections to include in a feed-forward track, when only the top N highest quality detections are to be processed in the downstream component. The number of detections to feed forward for each track is defined by the `FEED_FORWARD_TOP_CONFIDENCE_COUNT` property.
+
+The detection quality is also used in artifact extraction, where the user may want to get artifacts for the top N highest quality detections. This is controlled with the `ARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT` property. The detections in a track will be sorted by their confidence value, and then the N detections with the highest confidence will be extracted, up to the number of available detections, where N is an integer greater than 0.
+
+# Quality Selection Property
+
+Prior to release 9.0 of OpenMPF, the workflow manager was restricted to using detection confidence as its measure of quality. As mentioned above, for certain detection algorithms, there may be other detection properties that serve as a better measure of detection quality. With release 9.0, the user may now specify what property to use for all of the quality selection functionality mentioned above. 
+
+For all OpenMPF components, there are now two properties that are used to control quality selection. The first is named `QUALITY_SELECTION_PROPERTY`. It is a string that defines the name of the property to use for quality selection. If this property is set to `CONFIDENCE`, then the `confidence` member of the OpenMPF `ImageLocation` object is used, just as it did in releases prior to 9.0. If it is set to any other string, then the workflow manager will search the `detection_properties` map in each `ImageLocation` for an entry with that key, and use the corresponding value as the detection quality. The value associated with this property must be an integer or floating point value, where higher values indicate higher quality.
+
+The second property that is used in quality selection is named `QUALITY_SELECTION_THRESHOLD`. This threshold is used for filtering out low quality detections and tracks. This property replaces the `CONFIDENCE_THRESHOLD` property. If the `QUALITY_SELECTION_PROPERTY` is set to `CONFIDENCE`, this property should be set to the corresponding confidence threshold value.
+
+For consistency, the two properties `FEED_FORWARD_TOP_CONFIDENCE_COUNT` and `ARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT` have been renamed to `FEED_FORWARD_TOP_QUALITY_COUNT` and `ARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT`, respectively.
+
+
+# Hybrid Quality Selection Properties
+
+In some cases, there may be a detection property that a component would like to use as a measure of quality but it doesn't lend itself to simple thresholding. For example, a face detector might be able to calculate the face pose, and would like to select faces that are in the most frontal pose as the highest quality detections. The yaw of the face pose may be used to indicate this, but if it's values are between, say, -90 degrees and +90 degrees, then the highest quality detection would be the one with a value of yaw closest to 0. This violates the need for the quality selection property to take on a range of values where the highest value indicates the highest quality.
+
+Another use case might be where the component would like to choose detections based on a set of quality values, or properties. Continuing with the face pose example, the component might like to designate the detection with pose closest to frontal as the highest quality, but would also like to assign high quality to detections where the pose is closest to profile, meaning values of yaw closest to -90 or +90 degrees.
+
+In both of these cases, the component can create a custom detection property that is used to rank these detections as it sees fit. It could use a detection property called `RANK`, and assign values to that property to rank the detections from lowest to highest quality. In the example of the face detector wanting to use the yaw of the face pose, the detection with a value of yaw closest to 0 would be assigned a `RANK` property with the highest value, then the detections with values of yaw closest to +/-90 degrees would be assigned the second and third highest values of `RANK`. Detections without the `RANK` property would be treated as having the lowest possible quality value. Thus, the track exemplar would be the face with the frontal pose, and the `ARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT` property could be set to 3, so that the frontal and two profile pose detections would be kept as track artifacts.
+