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On-line tool wear monitoring is essential to automatic machining process. In order to predict tool wear accurately and reliably under different cutting conditions, a novel tool wear monitoring system (TWMS) is proposed by using localized fuzzy neural networks(LFNN) in this study which may improve classification accuracy of tool states and the computing speed compared with BPNN and normal fuzzy neural networks in the process of turning. By analyzing cutting forces signals and acoustic emission signals in time domain, frequency domain, and time-frequency domain, a series of features that sensitive to tool states were selected as inputs of neural networks according to synthesis coefficient. The nonlinear relations between tool wear and features were modeled by using integrated neural network (INN) that constructed and optimized through LFNN trained by an adaptive learning algorithm. The experimental results show that the monitoring system based on LFNN is provided with high precision, rapid computing speed and good multiplication.