In order to realize the online monitoring and accurate identification of picks wear degree in the cutting process, a new method based on multifeature information fusion was proposed for identifying shearer pick wear degree. The vibration and acoustic emission signals in the cutting process of different picks wear degree were analyzed by time domain analysis and wavelet packet analysis. According to the features of two adjacent pick wear degrees, there were data intersections for characteristic samples, which increased the difficulty of system identification. The optimal fuzzy membership function for each characteristic signal was calculated by using the least fuzzy optimization model, the maximum membership degree of the feature sample was obtained. The backpropagation(BP) neural network recognition model was trained and learned by using multifeature data samples. The experimental results show that the results of network discrimination are consistent with actual wear level of test sample. It can accurately monitor and identify the type of picks wear. The research results have great significance to monitoring and replacement of picks in actual engineering.