Event anaphora resolution plays an important role in discourse analysis. In comparison with general noun phrases, pronouns carry little information of themselves, resolving the event pronouns is a more difficult task. This paper proposes a machine learning-based framework for event pronoun resolution. All kinds of features, including both flat and structural features, are explored for event pronoun resolution. Experiments on OntoNotes corpus show that both flat and structural features are very effective for this task.