The multiplicity of operation phases is inherent in the nature of many batch processes, and each phase exhibits significantly different underlying behaviors. In addition, within each phase, normal processes in general follow certain underlying operation rules, called inner-phase evolution here, which however have not been addressed before. In this paper, a new statistical modeling and online monitoring method is proposed for multiphase batch processes. A two-level phase division algorithm is proposed to capture the process trend and trace inner-phase evolutions. It reveals that the inner-phase process in general goes through three statuses sequentially, i.e., transition, steady phase, and transition. Principal component analysis (PCA) and qualitative trend analysis (QTA) are combined to distinguish different inner-phase process statuses. Their different characteristics are then modeled and monitored separately, revealing more accurate process operation information. Meanwhile, the problem of uneven-duration batches is effectively handled in different inner-phase process statuses. The application to a typical multiphase batch process, injection molding, illustrates the feasibility and performance of the proposed algorithm.