AbstractThe Kepler Mission simultaneously measures the brightness of more than 150,000 stars every 29.4 minutes primarily for the purpose of transit photometry. Over the course of its 3.5-year primary mission Kepler has observed over 190,000 distinct stars, announcing 2,321 planet candidates, 2,165 eclipsing binaries, and 105 confirmed planets. As Kepler moves into its 4-year extended mission, the total number of transit-like features identified in the light curves has increased to as many as ~18,000. This number of signals has become intractable for human beings to inspect by eye in a thorough and timely fashion. To mitigate this problem we are developing machine learning approaches to perform the task of reviewing the diagnostics for each transit signal candidate to establish a preliminary list of planetary candidates ranked from most credible to least credible. Our preliminary results indicate that random forests can classify potential transiting planet signatures with an accuracy of more than 98.6% as measured by the area under a receiver-operating curve.