For the effective monitoring of batch processes with uneven multiphases, phase partitioning and discriminant analysis are two critical problems. To fully solve these two problems, a systematic strategy including fuzzy phase partitioning and hybrid discriminant analysis is proposed First, using a new unsupervised, multiscale, sequential partition (UMSP), each batch is divided into phases with transitions using different clustering scales. On this basis, two multiple-support-vector-data-description (SVDD) models are built for online phase partitioning and monitoring, and a hybrid-discriminant-analysis method is then developed for online fault detection. The effectiveness and advantages of the proposed method are illustrated with a 2D, handwritten example and a fed-batch penicillin-fermentation process.