diff --git a/laserchicken/height_stats.py b/laserchicken/height_stats.py
new file mode 100644
index 0000000..c28cafa
--- /dev/null
+++ b/laserchicken/height_stats.py
@@ -0,0 +1,29 @@
+import numpy as np
+import scipy.stats.stats as stat
+
+from laserchicken.feature_extractor.abc import AbstractFeatureExtractor
+from laserchicken.keys import point
+
+
+class HeightStatistics(AbstractFeatureExtractor):
+    @classmethod
+    def requires(cls):
+        return []
+
+    @classmethod
+    def provides(cls):
+        return ['max_z', 'min_z', 'mean_z', 'median_z', 'std_z', 'var_z', 'range', 'coeff_var_z', 'skew_z', 'kurto_z']
+
+    def extract(self, sourcepc, neighborhood, targetpc, targetindex):
+        z = sourcepc[point]['z']['data'][neighborhood]
+        max_z = np.max(z)
+        min_z = np.min(z)
+        mean_z = np.mean(z)
+        median_z = np.median(z)
+        std_z = np.std(z)
+        var_z = np.var(z)
+        range_z = max_z - min_z
+        coeff_var_z = np.std(z) / np.mean(z)
+        skew_z = stat.skew(z)
+        kurto_z = stat.kurtosis(z)
+        return max_z, min_z, mean_z, median_z, std_z, var_z, range_z, coeff_var_z, skew_z, kurto_z
diff --git a/laserchicken/test_height_stats.py b/laserchicken/test_height_stats.py
new file mode 100644
index 0000000..72db367
--- /dev/null
+++ b/laserchicken/test_height_stats.py
@@ -0,0 +1,36 @@
+import os
+import random
+import unittest
+
+from laserchicken.height_stats import HeightStatistics
+from laserchicken import read_las, keys
+
+
+class TestHeightStats(unittest.TestCase):
+
+    def test_height_stats(self):
+        print(os.getcwd())
+        print(os.path.exists("testdata/AHN2.las"))
+        pc_in = read_las.read("testdata/AHN2.las")
+        indices = [89664, 23893, 30638, 128795, 62052, 174453, 29129, 17127, 128215, 29667, 116156, 119157, 98591, 7018,
+                   61494, 65194, 117931, 62971, 10474, 90322]
+        extractor = HeightStatistics()
+        (max_z, min_z, mean_z, median_z, std_z, var_z, range_z, coeff_var_z, skew_z, kurto_z) = extractor.extract(
+            pc_in, indices, None, None)
+        print(max_z, min_z, mean_z, median_z, std_z, var_z, range_z, coeff_var_z, skew_z, kurto_z)
+        assert (max_z == 5.979999973773956)
+        assert (min_z == 0.47999997377395631)
+        assert (mean_z == 1.3779999737739566)
+        assert (median_z == 0.69999997377395629)
+        assert (std_z == 1.3567741153191268)
+        assert (var_z == 1.8408359999999995)
+        assert (range_z == 5.5)
+        assert (coeff_var_z == 0.9845966191155302)
+        assert (skew_z == 2.083098281031817)
+        assert (kurto_z == 3.968414258629714)
+
+    def setUp(self):
+        random.seed(20)
+
+    def tearDown(self):
+        pass