Abstract: A new methodology of tool wear classification based on dynamic tree is proposed. The correlation coefficients approach is utilized to extract several features with a close relation to tool wear. B-spline neural networks charactered by local memory is introduced to establish the nonlinearity relation between tool wear amounts and monitoring features extracted from acoustic emission，dynamometer and vibration sensors. Tool wear monitoring systems is so built under arbitrary machining conditions, and the integrated neural networks give the final classifying results of tool wear. The experimental results indicate that the tool wear monitoring system founded on the methodology is provided with high precision，high reliability，good multiplication and rapid recognizing speed，so it is good for popularization in industry．

Key words: B-spline Fuzzy neural network, Dynamic tree, Integrated neural network, Tool wear