diff --git a/doc/examples/plot_hough_transform.py b/doc/examples/plot_hough_transform.py
index b74132da..b15df92e 100644
--- a/doc/examples/plot_hough_transform.py
+++ b/doc/examples/plot_hough_transform.py
@@ -59,7 +59,7 @@ References
 
 '''
 
-from skimage.transform import hough, hough_peaks, probabilistic_hough
+from skimage.transform import hough_line, hough_peaks, probabilistic_hough
 from skimage.filter import canny
 from skimage import data
 
@@ -77,7 +77,7 @@ idx = np.arange(25, 75)
 image[idx[::-1], idx] = 255
 image[idx, idx] = 255
 
-h, theta, d = hough(image)
+h, theta, d = hough_line(image)
 
 plt.figure(figsize=(8, 4))
 
diff --git a/doc/source/plots/hough_tf.py b/doc/source/plots/hough_tf.py
index 42aa9c48..ea2fc4bb 100644
--- a/doc/source/plots/hough_tf.py
+++ b/doc/source/plots/hough_tf.py
@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage.transform import hough
+from skimage.transform import hough_line
 
 img = np.zeros((100, 150), dtype=bool)
 img[30, :] = 1
@@ -11,7 +11,7 @@ for i in range(90):
     img[i, i] = 1
 img += np.random.random(img.shape) > 0.95
 
-out, angles, d = hough(img)
+out, angles, d = hough_line(img)
 
 plt.subplot(1, 2, 1)
 
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index d88d7dd0..0d7196c8 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -147,7 +147,7 @@ def hough_line(img, theta=None):
 
     Apply the Hough transform:
 
-    >>> out, angles, d = hough(img)
+    >>> out, angles, d = hough_line(img)
 
     .. plot:: hough_tf.py
 
@@ -187,12 +187,12 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
     Parameters
     ----------
     hspace : (N, M) array
-        Hough space returned by the `hough` function.
+        Hough space returned by the `hough_line` function.
     angles : (M,) array
-        Angles returned by the `hough` function. Assumed to be continuous
+        Angles returned by the `hough_line` function. Assumed to be continuous
         (`angles[-1] - angles[0] == PI`).
     dists : (N, ) array
-        Distances returned by the `hough` function.
+        Distances returned by the `hough_line` function.
     min_distance : int
         Minimum distance separating lines (maximum filter size for first
         dimension of hough space).
@@ -213,14 +213,14 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
     Examples
     --------
     >>> import numpy as np
-    >>> from skimage.transform import hough, hough_peaks
+    >>> from skimage.transform import hough_line, hough_peaks
     >>> from skimage.draw import line
     >>> img = np.zeros((15, 15), dtype=np.bool_)
     >>> rr, cc = line(0, 0, 14, 14)
     >>> img[rr, cc] = 1
     >>> rr, cc = line(0, 14, 14, 0)
     >>> img[cc, rr] = 1
-    >>> hspace, angles, dists = hough(img)
+    >>> hspace, angles, dists = hough_line(img)
     >>> hspace, angles, dists = hough_peaks(hspace, angles, dists)
     >>> angles
     array([  0.74590887,  -0.79856126])