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In this paper, a new process monitoring method based upon wavelet transform and blind source separation is presented. Wavelet transform is employed to de-noise measured signals to remove the process noise, and blind source separation based on information maximization is used to extract blind source signals. Later, control limits and monitoring plots are built by estimating the probability distribution of every blind signal by means of Parzen density estimator. For investigating the feasibility of this method, its fault-detection performance is evaluated and compared with other process monitoring method based on blind source analysis with direct process information to a simple AR(1) process and a continuous stirred-tank-reactor process. The results show the superiority of the method presented in this paper over other process monitoring method, which has high faulty warnings and missing warnings.