The multiblock strategy is widely used for plant-wide process monitoring, to capture the relations between complex process variables of the plant-wide process, however, the sub-block data obtained from the traditional multiblock method still has the problem of non-Gaussian and Gaussian mixture distribution, which influences the performance of process monitoring. Therefore, a plant-wide process monitoring method based on multiblock MICA-PCA is proposed to improve the process monitoring performance. Firstly, the measured variables are automatically divided into non-Gaussian block and Gaussian block through the Jarque-Bera(J-B) test method, the non-Gaussian block and Gaussian block are divided into non-Gaussian sub-blocks and Gaussian sub-blocks through the Hellinger Distance(HD) method. By using different modeling and diagnosis methods for non-Gaussian sub-blocks and Gaussian sub-blocks, the monitoring effect is improved. Finally, the proposed method is applied to monitor the Tennessee-Eastman(TE) process, which shows its effectiveness.