A novel fuzzy phase partition method and a hybrid modeling strategy are proposed for quality prediction and process monitoring in batch processes with multiple operation phases. The fuzzy phase partition method is proposed on the basis of a sequence-constrained fuzzy c-means (SCFCM) clustering algorithm. It divides the batch process into several fuzzy operation phases by performing the SCFCM algorithm on trajectory data of phase-sensitive process variables. This SCFCM-based partition method not only has high computation efficiency and good partition accuracy but also is easy to implement and popularize. In addition, it generates "soft" partition results, where a "transition" phase exists between two adjacent "steady" operation phases. A hybrid modeling strategy is developed to build appropriate models for all operation phases according to their own characteristics. Phase-based multiway PLS models are built for regular steady phases that have longer durations and stable process behaviors. Just-in-time PLS models are built for those phases with shorter durations but time-varying or nonlinear process behaviors, including all transition phases and several irregular steady phases. This hybrid modeling strategy significantly enhances the modeling accuracy, resulting in better quality prediction and process monitoring performance. Advantages of proposed methods are illustrated by case studies in a fed-batch penicillin fermentation process.