Hybrid intelligent data fusion for monitoring end milling tool wear is presented.Signals of cutting force and vibration are measured with multi-sensor and features in frequency domain and time-frequency domain are extracted by using wavelet package decomposition.Several hybrid intelligent data fusion methods

which are wavelet neural networks

generic algorithm neural networks（GA-NN） and wavelet generic algorithm neural networks for predicting tool wear value are debated.The results show experimently all of these presented methods effectively implement tool wear monitoring and prediction

and the characters of these methods are analyzed.