diff --git a/scikits/image/morphology/skeletonize.py b/scikits/image/morphology/skeletonize.py
index c5588370..853aba3e 100644
--- a/scikits/image/morphology/skeletonize.py
+++ b/scikits/image/morphology/skeletonize.py
@@ -6,6 +6,7 @@ Original author: Neil Yager
 
 import numpy as np
 from scipy.ndimage import correlate
+from .. import util
 
 def skeletonize(image):
     """
@@ -26,7 +27,8 @@ def skeletonize(image):
     
     image: ndarray (2D)
         A binary image containing the objects to be skeletonized. '1' 
-         represents foreground, and '0' represents background.
+         represents foreground, and '0' represents background. It 
+         also accepts arrays of boolean values where True is foreground.
     
     Notes
     -----
@@ -62,15 +64,24 @@ def skeletonize(image):
             3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
             2,3,1,3,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
             2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,2,2,0,0,2,0,0,0]
+
+    # convert to unsigned int (this should work for boolean values)
+    skeleton = np.array(image).astype(np.uint8)
+
+    # check some properties of the input image:
+    #  - 2D
+    #  - binary image with only 0's and 1's 
+    if skeleton.ndim != 2:
+        raise ValueError('Skeletonize requires a 2D array')    
+    for val in np.unique(skeleton):
+        if val not in [0, 1]:
+            raise ValueError('Invalid value in the image: %d'%(val))
     
-    # initialize the skeleton to the original image
-    # TODO: how to handle data types
-    skeleton = image.copy().astype(np.int8)
-    
-    # create the mask that will assign a value based on neighbouring pixels
+    # create the mask that will assign a unique value based on the
+    #  arrangement of neighbouring pixels
     mask = np.array([[  1,  2,  4],
                      [128,  0,  8],
-                     [ 64, 32, 16]], np.int8)
+                     [ 64, 32, 16]], np.uint8)
 
     pixelRemoved = True
     while pixelRemoved:
diff --git a/scikits/image/morphology/tests/test_skeletonize.py b/scikits/image/morphology/tests/test_skeletonize.py
new file mode 100644
index 00000000..5a6bacb9
--- /dev/null
+++ b/scikits/image/morphology/tests/test_skeletonize.py
@@ -0,0 +1,83 @@
+import unittest
+import numpy as np
+from scikits.image.morphology import skeletonize
+import numpy.testing
+from scikits.image.draw import draw
+from scipy.ndimage import correlate
+
+class TestSkeletonize(unittest.TestCase):
+    def test_skeletonize_no_foreground(self):
+        im = np.zeros((5,5))
+        result = skeletonize.skeletonize(im)
+        numpy.testing.assert_array_equal(result, np.zeros((5,5)))
+    
+    def test_skeletonize_wrong_dim1(self):
+        im = np.zeros((5))
+        self.assertRaises(ValueError, skeletonize.skeletonize, im)    
+
+    def test_skeletonize_wrong_dim2(self):
+        im = np.zeros((5, 5, 5))
+        self.assertRaises(ValueError, skeletonize.skeletonize, im)    
+
+    def test_skeletonize_not_binary(self):
+        im = np.zeros((5, 5))
+        im[0, 0] = 1
+        im[0, 1] = 2
+        self.assertRaises(ValueError, skeletonize.skeletonize, im)  
+        
+    def test_skeletonize_unexpected_value(self):
+        im = np.zeros((5, 5))
+        im[0, 0] = 2
+        self.assertRaises(ValueError, skeletonize.skeletonize, im)          
+        
+    def test_skeletonize_all_foreground(self):
+        im = np.ones((3,4))
+        result = skeletonize.skeletonize(im)
+    
+    def test_skeletonize_single_point(self):
+        im = np.zeros((5, 5), np.uint8)
+        im[3, 3] = 1
+        result = skeletonize.skeletonize(im)
+        numpy.testing.assert_array_equal(result, im)
+    
+    def test_skeletonize_already_thinned(self):
+        im = np.zeros((5, 5), np.uint8)
+        im[3,1:-1] = 1
+        im[2, -1] = 1
+        im[4, 0] = 1
+        result = skeletonize.skeletonize(im)
+        numpy.testing.assert_array_equal(result, im)
+    
+    def test_skeletonize_num_neighbours(self):
+        # an empty image
+        image = np.zeros((300, 300))
+        
+        # foreground object 1
+        image[10:-10, 10:100] = 1
+        image[-100:-10, 10:-10] = 1
+        image[10:-10, -100:-10] = 1
+        
+        # foreground object 2
+        rs, cs = draw.bresenham(250, 150, 10, 280)
+        for i in range(10): image[rs+i, cs] = 1 
+        rs, cs = draw.bresenham(10, 150, 250, 280)
+        for i in range(20): image[rs+i, cs] = 1 
+        
+        # foreground object 3
+        ir, ic = np.indices(image.shape)
+        circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2
+        circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2
+        image[circle1] = 1
+        image[circle2] = 0
+        result = skeletonize.skeletonize(image)
+        
+        # there should never be a 2x2 block of foreground pixels
+        #  in a skeleton
+        mask = np.array([[1,  1],
+                         [1,  1]], np.uint8)        
+        blocks = correlate(result, mask, mode='constant')
+        self.assertFalse(numpy.any(blocks == 4))
+        
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file