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Traditional process monitoring methods take all the measured variables into account, whereas it will be inappropriate for indicating quality-relevant faults. Some measured variables are independent from the quality variables and these redundancy variables will no doubt degrade the prediction performance of quality variables. This paper proposes a novel quality relevant and independent two block monitoring scheme based on mutual information (MI) and kernel principal component analysis (KPCA). First, all the process variables are divided into two subblocks according to their MI value with quality variables. Then, KPCA monitors the quality-relevant subblock and quality-independent subblock, respectively. When a fault is detected, kernel principal component regression is further utilized to obtain the predicted state of quality variables. Either of the information, whether the current fault disturbs quality-relevant variables or process quality, is necessary and important for engineers. The benefits of MI-KPCA are illustrated through a numerical simulation and the Tennessee Eastman process, and the results reveal the superiority of the proposal compared with some other monitoring methods.