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Although scarce, previous work on the application of machine learning and data mining techniques on large corpora of astronomical data has produced promising results. For example, on the task of detecting so-called Kepler objects of interest (KOIs), a range of di erent 'o the shelf' classi ers has demonstrated outstanding performance. ese rather preliminary research e orts motivate further exploration of this data domain. In the present work we focus on the analysis of threshold crossing events (TCEs) extracted from photometric data acquired by the Kepler spacecra . We show that the task of classifying TCEs as being e ected by actual planetary transits as opposed to confounding astrophysical phenomena is signi cantly more challenging than that of KOI detection, with di erent classi ers exhibiting vastly di erent performances. Nevertheless, the best performing classi er type, the random forest, achieved excellent accuracy, correctly predicting in approximately 96% of the cases. Our results and analysis should illuminate further e orts into the development of more sophisticated, automatic techniques, and encourage additional work in the area.