Integrated phase partition, online phase identification, and phase-based monitoring methods are proposed for multiphase batch processes with uneven durations. A new phase partition method is developed based on the warped K-means (WKM) clustering algorithm, which divides the entire batch into several operation phases by clustering the trajectory data of phase-sensitive process variables. This WKM-based phase partition method can efficiently cope with the sequentiality of batch data and, thus, ensures a reasonable phase partition result. Besides, because only phase-sensitive variables are used for phase partition, the phase partition accuracy is improved. An online phase identification method is proposed to identify the corresponding operation phase of a new sample according to a phase identification combination index (PICI). PICI quantifies the correlation of a new sample with each operation phase by calculating distance and time difference between the sample and the phase center. The PARAFAC2 and unfolded principal component analysis (uPCA) methods are applied to build monitoring models from the uneven-length batch data in each phase. T-2 and SPE statistics are constructed for fault detection. The contribution plot of T-2 statistic is used for fault diagnosis. The effectiveness and advantages of proposed methods are illustrated by the case study in a fed-batch penicillin fermentation process.