diff --git a/pycomlink/processing/wet_dry/mlp.py b/pycomlink/processing/wet_dry/mlp.py
new file mode 100644
index 0000000..8089418
--- /dev/null
+++ b/pycomlink/processing/wet_dry/mlp.py
@@ -0,0 +1,78 @@
+import numpy as np
+from numpy.lib.stride_tricks import sliding_window_view 
+import tensorflow as tf
+import pkg_resources
+
+def get_model_file_path():
+    return pkg_resources.resource_filename(
+        "pycomlink", "/processing/wet_dry/mlp_model_files"
+    )
+
+model = tf.keras.models.load_model(str(get_model_file_path() + "/model_mlp.keras"))
+
+def mlp_wet_dry(
+    trsl_channel_1,
+    trsl_channel_2,
+    threshold=None, # 0.5 is often good, or argmax
+):
+    """
+    Wet dry classification using a simple neural network based on channel 1 and channel 2 of a CML
+
+    Parameters
+    ----------
+    trsl_channel_1 : iterable of float
+         Time series of received signal level of channel 1
+    trsl_channel_2 : iterable of float
+         Time series of received signal level of channel 2
+    threshold : float 
+        Threshold (0 - 1) for setting event as wet or dry. 
+    
+
+    Returns
+    -------
+    iterable of float
+        Time series of wet/dry probability or (if threshold is provided) 
+        wet dry classification 
+        
+    References
+    ----------
+
+
+    """
+    # Normalization 
+    trsl_channel_1_norm = (trsl_channel_1 - np.nanmean(trsl_channel_1)) / np.nanstd(trsl_channel_1)
+    trsl_channel_2_norm = (trsl_channel_2 - np.nanmean(trsl_channel_2)) / np.nanstd(trsl_channel_2)
+
+    # add nan to start and end
+    windowsize = 40 # use two channels 
+    x_start = np.ones([int(windowsize/2), windowsize*2])*np.nan
+    x_end = np.ones([int(windowsize/2)- 1, windowsize*2])*np.nan
+    
+    # sliding window
+    sliding_window_ch1 = sliding_window_view(
+        trsl_channel_1_norm, 
+        window_shape = windowsize
+    )
+    
+    sliding_window_ch2 = sliding_window_view(
+        trsl_channel_2_norm, 
+        window_shape = windowsize
+    )
+
+    x_fts = np.vstack(
+        [x_start, np.hstack([sliding_window_ch1, sliding_window_ch2]), x_end]
+    )
+    
+    mlp_pred = np.zeros([x_fts.shape[0], 2])*np.nan
+    indices = np.argwhere(~np.isnan(x_fts).any(axis = 1)).ravel()
+    
+    if indices.size > 0: # everything is nan, mlp_pred is then all nan
+        mlp_pred_ = model.predict(x_fts[indices], verbose=0)
+        mlp_pred[indices] = mlp_pred_        
+    
+    if threshold == None:
+        return mlp_pred #
+    else:
+        mlp_pred = mlp_pred[:, 1]
+        mlp_pred[indices] = mlp_pred[indices] > threshold
+        return mlp_pred
diff --git a/pycomlink/processing/wet_dry/mlp_model_files/model_mlp.keras b/pycomlink/processing/wet_dry/mlp_model_files/model_mlp.keras
new file mode 100644
index 0000000..ba09e6c
Binary files /dev/null and b/pycomlink/processing/wet_dry/mlp_model_files/model_mlp.keras differ
diff --git a/pycomlink/tests/test_wet_dry_mlp.py b/pycomlink/tests/test_wet_dry_mlp.py
new file mode 100644
index 0000000..2f58bd4
--- /dev/null
+++ b/pycomlink/tests/test_wet_dry_mlp.py
@@ -0,0 +1,68 @@
+import unittest
+import numpy as np
+from pycomlink.processing.wet_dry.mlp import mlp_wet_dry
+
+class Testmlppred(unittest.TestCase):
+    def test_mlppred(self):
+        # generate random array
+        trsl_channel_1 = np.arange(0, 60 * 8).astype(float)
+        trsl_channel_2 = np.arange(0, 60 * 8).astype(float)
+
+        trsl_channel_1[310] = np.nan # shorter window than in cnn
+
+        pred_raw = mlp_wet_dry(
+            trsl_channel_1,
+            trsl_channel_2,
+            threshold=None,
+        )[:, 1]
+
+        pred = mlp_wet_dry(
+            trsl_channel_1,
+            trsl_channel_2,
+            threshold=0.1, # low threshold for testing
+        )
+
+        # check if length of array is the same
+        assert len(pred_raw) == 60 * 8
+        assert len(pred) == 60 * 8
+
+        # check if array is as expected
+        truth_raw = np.array(
+            [
+                0.08784304,
+                0.08941595,
+                0.09101421, 
+                0.09263814, 
+                0.09428804, 
+                0.09596423,
+                0.09766698, 
+                0.09939668, 
+                0.10115347, 
+                0.10293788, 
+                0.10475004,        
+                np.nan,
+                np.nan,
+            ]
+        )
+        truth = np.array(
+            [
+                0,
+                0,  
+                0,  
+                0,  
+                0,  
+                0,  
+                0,  
+                0,  
+                1,
+                1,  
+                1, 
+                np.nan, 
+                np.nan,
+            ]
+        )
+
+        np.testing.assert_almost_equal(pred[280:293], truth)   
+        np.testing.assert_almost_equal(
+            np.round(pred_raw, decimals=7)[280:293], truth_raw
+        )