Abstract: According to the condition that most practical process data can not meet the needs of normal distribution and many process monitoring methods analyze data at a single scale,this paper presents an improved process(monitoring) method named as multi-scale independent component analysis(MSICA) based on wavelet transform.The method analyzes initial data at different scales carefully,extracts independent signals according to the information maximization criterion,and monitors the process in real time in a low-dimensional subspace of data.Results of the simulation in Tennessee-Eastman(TE) process verify the efficiency of the method.