The full automation of machine tools has gained substantial importance in manufacturing industries in recent years, as machining technology has progressed from manually operated production machines to highly advanced and sophisticated CNC machine tool. Whereas manufacturing technology has moved to the stage of automation, there is still an unsolved problem in metal cutting processes: cutting chatter. Due to its complexity, thus cutting chatter is still the primary problem in metal cutting processes. According to the characteristic of cutting chatter, a real time monitoring technique of cutting chatter based on fuzzy hidden Markov model (FHMM) was presented. Hidden Markov model (HMM) is a state-of-the-art technique for speech recognition because of its elegant mathematical structure and the availability of computer implementation of these models. In this paper, the fuzzy EM algorithm was used to the Baum-Welch algorithm in the HMM method, and the strategy of time frequency feature extraction to non-stability signal was described. The experimental results show that the proposed method is feasible and effective for the monitoring of cutting chatter in the metal cutting processes.