A hovel process monitoring method is proposed based on sparse principal component analysis (SpPCA). To reveal meaningful variable correlations from process data, the SpPCA is developed to sequentially extract a set of sparse loading vectors from process data. To build a high-performance monitoring model, a fault detectability matrix is applied to select the sparse loading vectors used for process modeling from all sparse loading vectors obtained by SpPCA. The fault detectability matrix ensures that the faults related to any monitored process variable are detectable in the principal component subspace and no overlapped (or redundant) loading vectors are involved in the monitoring model. Moreover, the selected sparse loading vectors classify all process variables into nonoverlapping groups according to variable correlations. Two-level contribution plots, which consist of group-wise and group-variable-wise contribution plots, are used for fault diagnosis. The first-level group-wise contribution plot describes the individual contribution of each variable group to the fault. The second-level group variable-wise contribution plot reflects the individual contribution of each process variable to the fault. The two-level contribution plots not only utilize meaningful correlations between process variables in the same group, but also effectively remove the interference from process variables in other groups. Therefore, the fault diagnosis reliability and accuracy are significantly improved. The implementation, performance, and advantages of the proposed methods are illustrated with an industrial case study.