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To obtain comprehensive evaluations of major flank wear of a helical cutter in the milling process, the wear land area was proposed as an index for estimating the wear out of milling cutters. In the research, 8 dimensionless characteristic parameters, which are sensitive to major flank wear condition of the cutter, were extracted, selected and normalized as input signals of the wear condition monitoring system based on neural network information infusion method. By three-layer back propagation neural network model, with its capability of multi-sensor information infusion, major flank wear land width and wear land area of the helical cutter were monitored online. The output results of the monitoring system were consistent with the tested data.