Operating at different manufacturing steps, multiphase modeling and analysis of the batch process are advantageous to improving monitoring performance and understanding manufacturing processes. Although many phase partition algorithms have been proposed, they have some disadvantages and cause problems: (1) time sequence disorder, which requires elaborate post-treatments; (2) a lack of quantitative index to indicate transition patterns; and (3) tunable parameters that cannot be quantitatively determined. To effectively overcome these problems, an iterative two-step sequential phase partition algorithm is proposed in the present work. In the first step, initial phase partition results are obtained by checking changes of the control limit of squared prediction error. Sequentially, the fast search and find of density peaks clustering algorithm is employed to adjust the degradation degree and update the phase partition results. These two steps are iteratively executed until a proper degradation degree is found for the first phase. Then, the remaining phases are processed one by one using the same procedure. Moreover, a statistical index is quantitatively defined based on density and distance analysis to judge whether a process has transitions, and when the transition regions begin and end. In this way, the phases and transition patterns are quantitatively determined without ambiguity from the perspective of monitoring performance. The effectiveness of the proposed method is illustrated by a numerical example and a typical industrial case. Several typical phase partition algorithms are also employed for comprehensive comparisons with the proposed method. (c) 2016 American Institute of Chemical Engineers