The independent component analysis(ICA) is a multivariate statistical analysis method, which is often used for non-Gaussian process monitoring. It can effectively use the high-order statistical information (exceeding the third order) of the signal to extract independent components, which has been widely used in industrial process monitoring and is a research hotspot in the current international process monitoring field. As such, this paper introduces the classic ICA model, the improved ICA model and its process monitoring technology in industrial processes. Firstly, the classic ICA model is introduced, which is then classified and the advantages and disadvantages are pointed out. Secondly, in view of the shortcoming of the classic ICA model, the development of the improved ICA model is sorted out from three aspects, including ICA's own problems, noise and outliers. Then, the industrial process is applied as the main application background, and the ICA process monitoring technology is evolved from simple to complex industrial processes. Faced with the single and mixed characteristics of operating data in the industrial process, the current research status of the ICA and its extended models in industrial process monitoring are reviewed. Finally, the problems to be solved in this research field and the future development directions are discussed.