diff --git a/doc/source/api.rst b/doc/source/api.rst
index a4be0df5f489e..7e863a4429487 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -126,7 +126,7 @@ Data manipulations
    merge
    concat
 
-Top-level Missing Data
+Top-level missing data
 ~~~~~~~~~~~~~~~~~~~~~~
 
 .. currentmodule:: pandas.core.common
@@ -137,6 +137,17 @@ Top-level Missing Data
    isnull
    notnull
 
+Top-level dealing with datetimes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. currentmodule:: pandas.tseries.tools
+
+.. autosummary::
+   :toctree: generated/
+
+   to_datetime
+
+
 Standard moving window functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index f8d1e8323b9f5..f11bf60549d93 100644
--- a/doc/source/timeseries.rst
+++ b/doc/source/timeseries.rst
@@ -110,6 +110,68 @@ scalar values and ``PeriodIndex`` for sequences of spans. Better support for
 irregular intervals with arbitrary start and end points are forth-coming in
 future releases.
 
+
+.. _timeseries.converting:
+
+Converting to Timestamps
+------------------------
+
+To convert a Series or list-like object of date-like objects e.g. strings,
+epochs, or a mixture, you can use the ``to_datetime`` function. When passed
+a Series, this returns a Series (with the same index), while a list-like
+is converted to a DatetimeIndex:
+
+.. ipython:: python
+
+    to_datetime(Series(['Jul 31, 2009', '2010-01-10', None]))
+
+    to_datetime(['2005/11/23', '2010.12.31'])
+
+If you use dates which start with the day first (i.e. European style),
+you can pass the ``dayfirst`` flag:
+
+.. ipython:: python
+
+    to_datetime(['04-01-2012 10:00'], dayfirst=True)
+
+    to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
+
+.. warning::
+
+   You see in the above example that ``dayfirst`` isn't strict, so if a date
+   can't be parsed with the day being first it will be parsed as if
+   ``dayfirst`` were False.
+
+
+Pass ``coerce=True`` to convert bad data to ``NaT`` (not a time):
+
+.. ipython:: python
+
+   to_datetime(['2009-07-31', 'asd'])
+
+   to_datetime(['2009-07-31', 'asd'], coerce=True)
+
+It's also possible to convert integer or float epoch times. The default unit
+for these is nanoseconds (since these are how Timestamps are stored). However,
+often epochs are stored in another ``unit`` which can be specified:
+
+
+.. ipython:: python
+
+   to_datetime([1])
+
+   to_datetime([1, 3.14], unit='s')
+
+.. note::
+
+   Epoch times will be rounded to the nearest nanosecond.
+
+Take care, ``to_datetime`` may not act as you expect on mixed data:
+
+.. ipython:: python
+
+   pd.to_datetime([1, '1'])
+
 .. _timeseries.daterange:
 
 Generating Ranges of Timestamps