Abstract: A method of monitoring the wear of milling cutters is developed. An acoustic emission sensor is used for signal acquisition. Wavelet transform is used for signal feature extraction and the features are the scaling coefficient and the wavelet coefficients of AE signal. Sugeno fuzzy controller is adopted to recognize the features above corresponding to different amounts of tool wear, and the output of the controller is the tool wear (in mm). This makes the various real time compensations for the tool wear more convenient. Experiments show that Sugeno fuzzy controller can assess the tool wear both reliably and efficiently.