Abstract: In this paper, a new process monitoring method is presented based upon wavelet transform and blind source separation. At first, wavelet transform is employed to de-noise measured signals to remove the process noise. Then blind source separation based on information maximization is used to extract blind source signals of the process. After this, process 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 method also based on blind source analysis directly with process information by applying it to 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 false alarms and missing alarms.