Abstract: The sensitive features of AE were properly chosen to monitor tool flank wear. Based on fuzzy classification the features of AE signal were optimized, and on this basis a general conclusion of optimizing AE characteristics was reached. The method of using fuzzy classification to optimize the AE signal feature and the optimum features were presented and proved in the experiment of real time detecting tool wear. The experiments showed that using fuzzy classification can effectively optimize AE features.