On-line chatter detection can avoid unstable cutting through monitoring the machining process. In order to identify chatter in a timely manner, an improved Support Vector Machine (SVM) is developed in this paper, based on extracted features. In the SVM model, the penalty factor (c) and the core parameter (g) have important influence on the classification, more than from Kernel Functions (KFs). Hence, first the classification results are conducted using different KFs. Then two methods are presented for exploring the best parameters. The chatter identification results show that the Genetic Algorithm (GA) approach is more suitable for deciding the parameters than the Grid Explore (GE) approach.