Between-phase transition analysis and monitoring are a critical problem in multiphase (MP) batch processes. An improved statistical analysis, modeling, and monitoring strategy are proposed for MP processes with between-phase transition. It is realized that between-phase transition may show complex irregular dynamics over different batches. That is, transition patterns may follow different trajectories with different durations and reveal different characteristics in different batch cycles. Phase centers are defined to capture the transition irregularity, and the relationship between two neighboring phase centers is analyzed by performing between-phase analysis. Two different subspaces are thus separated in each phase, driven by the phase-common and dependent correlations, respectively. The basic assumption is that despite their different operation patterns, the two neighboring phases share a certain common correlations immune to phase shift. Then, reconstruction-based transition identification algorithm is designed, by which, between-phase transition can be supervised automatically and dynamically without the need of transition model development. The proposed method captures the between-phase transition from a new viewpoint. Its feasibility and performance are illustrated with a practical case. (c) 2011 American Institute of Chemical Engineers AIChE J, 2012