In this paper, a novel variable moving windows based non-Gaussian dissimilarity analysis technique is developed to handle the challenges related to the batch processes such as non-linearity, non-Gaussianity and time-varying dynamics. First, the independent component analysis (ICA) models are developed on the normal reference data sets and the monitored data sets to extract the dominant independent component subspaces through a variable moving windows strategy. Then, non-Gaussian dissimilarity indices are computed to evaluate the statistical independency of the extracted IC subspaces at each specific time interval. Thus, the process non-Gaussian features are fully captured and the process trajectories distribution information of batch-to-batch is quantitatively estimated for online process monitoring. Further, the non-Gaussian contribution index based on the mutual information is introduced to identify the variables that may be responsible for the process abnormality. The reliability and validity of the proposed method are verified on the fed-batch Penicillin Fermentation process. The application results present superior fault detection and diagnosis performance compared with the PCA dissimilarity, MPCA and MICA approaches.