Error-handling (EH) code snippets are widely used for troubleshooting in software projects. Analyzing these snippets help to better understand how developers handle errors. However, the identification of such error-handling code snippets from the large-scale software is non-trivial, since traditional methods meet a challenge of scalability. In this paper, we analyze a large number of error-handling code snippets and get same interesting and useful observations. We extract seven features according to these observations. Based on these features, we design an automatic approach to identify error-handling codes using static program analysis and machine learning algorithms. Finally, we evaluate this approach and select the optimal feature subset from all feature combinations. Our evaluation demonstrates the high F-Score of up to 0.85 in identifying error-handling code snippets.