Abstract: Aiming at the batch process that has the non-Gaussian distribution or mixed distribution, a new monitoring method based on modified MICA-PCA is researched. Process information of non-Gaussian is first extracted using the MICA method. Setting threshold value of negative entropy is used to automatically select the independent components, which can overcome the shortcoming of predefining the number of independent components in traditional method of ICA. The confidence limits of the corresponding monitoring statistics are determined using kernel density estimation; then the process residual information, which is multivariate Gaussian distribution, is further analyzed and processed using PCA. The method is applied to the fermentation process monitoring of obtaining interleukin by recombinant Escherichia coli, in a biochemical pharmaceutical factory in Beijing.Resultsshow that when process variables are not Gaussian distribution, the method can accurately monitor the process and effectively reduce the alarm failure and false alarm of traditional method.