diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx
index d0e4c2a3..dfb37a7c 100644
--- a/skimage/segmentation/_slic.pyx
+++ b/skimage/segmentation/_slic.pyx
@@ -3,7 +3,7 @@ cimport numpy as np
 from time import time
 from scipy import ndimage
 from ..util import img_as_float
-from ..color import rgb2lab
+from ..color import rgb2lab, gray2rgb
 
 
 def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
@@ -12,7 +12,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
 
     Parameters
     ----------
-    image : (width, height, 3) ndarray
+    image : (width, height [, 3]) ndarray
         Input image.
     n_segments : int
         The (approximate) number of labels in the segmented output image.
@@ -53,9 +53,10 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
     >>> # Increasing the ratio parameter yields more square regions
     >>> segments = slic(img, n_segments=100, ratio=20)
     """
-    image = np.atleast_3d(image)
-    if image.shape[2] != 3:
-        ValueError("Only 3-channel 2D images are supported.")
+    if image.ndim == 2:
+        image = gray2rgb(image)
+    if image.ndim != 3 or image.shape[2] != 3:
+        ValueError("Only 1- or 3-channel 2D images are supported.")
     image = ndimage.gaussian_filter(img_as_float(image), [sigma, sigma, 0])
     if convert2lab:
         image = rgb2lab(image)
diff --git a/skimage/segmentation/tests/test_slic.py b/skimage/segmentation/tests/test_slic.py
index f2d6698d..59088fcd 100644
--- a/skimage/segmentation/tests/test_slic.py
+++ b/skimage/segmentation/tests/test_slic.py
@@ -21,6 +21,22 @@ def test_color():
     assert_array_equal(seg[:10, 10:], 1)
     assert_array_equal(seg[10:, 10:], 3)
 
+def test_gray():
+    rnd = np.random.RandomState(0)
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    img[10:, :10] = 0.67
+    img[10:, 10:] = 1.00
+    img += 0.0033 * rnd.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(img, sigma=0, n_segments=4, ratio=50.0)
+    print(seg)
+    assert_equal(len(np.unique(seg)), 4)
+    assert_array_equal(seg[:10, :10], 0)
+    assert_array_equal(seg[10:, :10], 2)
+    assert_array_equal(seg[:10, 10:], 1)
+    assert_array_equal(seg[10:, 10:], 3)
 
 if __name__ == '__main__':
     from numpy import testing