diff --git a/numpydoc/docscrape.py b/numpydoc/docscrape.py
index 807e9bc0..0f60a8cf 100644
--- a/numpydoc/docscrape.py
+++ b/numpydoc/docscrape.py
@@ -220,7 +220,7 @@ def _parse_param_list(self, content, single_element_is_type=False):
         while not r.eof():
             header = r.read().strip()
             if ' : ' in header:
-                arg_name, arg_type = header.split(' : ')[:2]
+                arg_name, arg_type = header.split(' : ', maxsplit=1)
             else:
                 if single_element_is_type:
                     arg_name, arg_type = '', header
diff --git a/numpydoc/tests/test_docscrape.py b/numpydoc/tests/test_docscrape.py
index f74a2a58..6165bd89 100644
--- a/numpydoc/tests/test_docscrape.py
+++ b/numpydoc/tests/test_docscrape.py
@@ -45,6 +45,8 @@
       Given a shape of, for example, (m,n,k), m*n*k samples are
       generated, and packed in an m-by-n-by-k arrangement.  Because
       each sample is N-dimensional, the output shape is (m,n,k,N).
+  dtype : data type object, optional (default : float)
+      The type and size of the data to be returned.
 
   Returns
   -------
@@ -180,7 +182,7 @@ def test_extended_summary():
 
 
 def test_parameters():
-    assert len(doc['Parameters']) == 3
+    assert len(doc['Parameters']) == 4
     names = [n for n, _, _ in doc['Parameters']]
     assert all(a == b for a, b in zip(names, ['mean', 'cov', 'shape']))
 
@@ -189,6 +191,17 @@ def test_parameters():
     assert desc[0].startswith('Covariance matrix')
     assert doc['Parameters'][0][-1][-1] == '   (1+2+3)/3'
 
+    arg, arg_type, desc = doc['Parameters'][2]
+    assert arg == 'shape'
+    assert arg_type == 'tuple of ints'
+    assert desc[0].startswith('Given')
+    assert doc['Parameters'][0][-1][-1] == '   (1+2+3)/3'
+
+    arg, arg_type, desc = doc['Parameters'][3]
+    assert arg == 'dtype'
+    assert arg_type == 'data type object, optional (default : float)'
+    assert desc[0].startswith('The type and size')
+
 
 def test_other_parameters():
     assert len(doc['Other Parameters']) == 1
@@ -205,7 +218,7 @@ def test_returns():
     assert arg_type == 'ndarray'
     assert desc[0].startswith('The drawn samples')
     assert desc[-1].endswith('distribution.')
-
+    
     arg, arg_type, desc = doc['Returns'][1]
     assert arg == ''
     assert arg_type == 'list of str'
@@ -394,6 +407,8 @@ def test_str():
     Given a shape of, for example, (m,n,k), m*n*k samples are
     generated, and packed in an m-by-n-by-k arrangement.  Because
     each sample is N-dimensional, the output shape is (m,n,k,N).
+dtype : data type object, optional (default : float)
+    The type and size of the data to be returned.
 
 Returns
 -------
@@ -560,6 +575,9 @@ def test_sphinx_str():
         generated, and packed in an m-by-n-by-k arrangement.  Because
         each sample is N-dimensional, the output shape is (m,n,k,N).
 
+    **dtype** : data type object, optional (default : float)
+        The type and size of the data to be returned.
+
 :Returns:
 
     **out** : ndarray