The traditional principal component analysis (PCA)-based process monitoring method builds a global statistical model and omits the mining of the local variable behaviours, which may degrade the fault detection performance. Considering this problem, this paper proposes a variable weight information-based multi-block PCA (VWI-MBPCA) method. Firstly, a sequence hierarchical clustering algorithm is proposed to divide the full PCA component space into several sub-blocks, where the components sharing similar variable weight information are gathered together and then the sub-block T-2 statistic is constructed for monitoring sub-block components. Further, the variables with small weight information on each component sub-block are extracted to build an additional sub-PCA model, where the 2 statistic is developed to compensate the sub-block T-2 statistic. In order to integrate the monitoring results of each sub-block, Bayesian inference is applied to construct an overall T-2 statistic. To identify the faulty variables, a multi-block PCA contribution plot is designed by choosing some specific blocks to highlight fault information. Finally, simulations on a numerical example and the benchmark Tennessee Eastman (TE) process are used to demonstrate the strengths of the proposed method.