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In order to improve the accuracy and speed of on-line tool wear monitoring system, an evolutionary neural network using variable string genetic algorithm (VGA) was developed to construct the relations between tool wear and signal features extracted from cutting forces, vibrations, and acoustic emission by different signal processing methods. The system could automatically evolve the appropriate architecture of neural network and find a near-optimal set of connection weights globally. Then the conformable connection weights for model could be found with back-propagation (BP) algorithm, the multi-model finally completed calculation of tool wear. The experimental results show that the system proposed in the paper has higher classification precision and calculating speed.