An optimized design of binary phase-only matched filters used in optical pattern recognition by means of Clipping and Monte Carlo learning arithmetic in the neural network is introduced. The computer simulation result indicates that compared with the correlation output of the traditional phase-only matched filter, their signal-to-noise ratio (SNR) for recognizing output and the signal correlation peak are improved obviously. Thereby it may provide a favorable foundation for the optical implementation in the future.