diff --git a/seaborn/matrix.py b/seaborn/matrix.py
index ff94bea352..1028589527 100644
--- a/seaborn/matrix.py
+++ b/seaborn/matrix.py
@@ -1,8 +1,12 @@
 """Functions to visualize matrices of data."""
 import itertools
+from collections import OrderedDict
 
 import matplotlib as mpl
 from matplotlib.collections import LineCollection
+from matplotlib.cm import ScalarMappable
+from matplotlib.colors import LinearSegmentedColormap, Normalize, rgb2hex
+from matplotlib import patches as mpatches
 import matplotlib.pyplot as plt
 from matplotlib import gridspec
 import numpy as np
@@ -89,6 +93,85 @@ def _matrix_mask(data, mask):
     return mask
 
 
+def _color_annotation(df, colors, bg_color='#ffffff'):
+    """Converts a data frame of annotations to colors."""
+
+    colored_cols = OrderedDict()
+    color_maps = OrderedDict()
+
+    for (col_name, values), color in zip(df.items(), colors):
+        colored, color_map = _color_column(values, color, bg_color)
+        colored_cols[col_name] = colored
+        color_maps[col_name] = color_map
+
+    return pd.DataFrame(colored_cols), color_maps
+
+
+def _color_column(series, color, bg_color):
+    """Converts an annotation column to colors."""
+    if series.dtype == bool:
+        # Boolean case.
+        return _color_bool_column(series, color, bg_color)
+    elif series.dtype.kind in 'biufc':
+        # Numeric case.
+        return _color_numeric_column(series, color, bg_color)
+    elif series.dtype.kind == 'O':
+        # Strings and categorical case..
+        return _color_cat_column(series, color, bg_color)
+    else:
+        raise ValueError('Unsupported dtype: {}'.format(series.dtype))
+
+
+def _color_bool_column(series, color, bg_color):
+    """Converts a boolean annotation column to colors."""
+    return _color_numeric_column(series.astype(int), color, bg_color)
+
+
+def _color_numeric_column(series, color, bg_color, n=200):
+    """Converts a numeric annotation column to colors."""
+
+    cmap = LinearSegmentedColormap.from_list(
+        name='custom_cmap', colors=[bg_color, color], N=n)
+    norm = Normalize(vmin=series.min(), vmax=series.max())
+    mappable = ScalarMappable(norm=norm, cmap=cmap)
+
+    rgba_colors = mappable.to_rgba(series.values)
+    hex_colors = (rgb2hex(rgba) for rgba in rgba_colors)
+
+    mapped = pd.Series(hex_colors, index=series.index, name=series.name)
+
+    return mapped, color
+
+
+def _color_cat_column(series, colors, bg_color):
+    """Converts a categorical annotation column to colors."""
+
+    if series.dtype.name == 'category' and series.cat.ordered:
+        str_values = series.cat.categories
+    else:
+        str_values = set(series.dropna())
+
+    if isinstance(colors, list):
+        colors = [_rgb_to_hex(c, normalized=True) for c in colors]
+        color_map = dict(zip(str_values, colors))
+        mapped = series.map(color_map).fillna(bg_color)
+    else:
+        numeric_values = np.linspace(0, 1, len(str_values) + 1)[1:]
+        numeric_map = dict(zip(str_values, numeric_values))
+
+        mapped = _color_numeric_column(series.map(numeric_map),
+                                       colors, bg_color=bg_color)
+        color_map = dict(zip(series, mapped.values))
+
+    return mapped, color_map
+
+
+def _rgb_to_hex(rgb, normalized=True):
+    if normalized:
+        rgb = tuple(map(lambda x: int(x * 255), rgb))
+    return '#%02x%02x%02x' % rgb
+
+
 class _HeatMapper(object):
     """Draw a heatmap plot of a matrix with nice labels and colormaps."""