Several studies have applied the hidden Markov model (HMM) in multimode process monitoring. However, because the inherent duration probability density of HMM is exponential, which is inappropriate for modeling the multimode process, the performance of these HMM-based approaches is not satisfactory. As a result, the hidden semi-Markov model (HSMM), which integrated the mode duration probability into HMM, is combined with principal component analysis (PCA) to handle the multimode feature, named as HSMM-PCA. PCA is a powerful monitoring algorithm for the unimodal process, and HSMM specializes in mode division and identification. HSMM-PCA inherits the advantages of these two algorithms and hence it performs much better than the existing HMM-based approaches do. In addition, HSMM-PCA can detect the mode disorder fault, which challenges the most multimode approaches.