The exiting automatic phase partition and phase-based process monitoring strategies are in general limited to single-mode multiphase batch processes. In this article, a concurrent phase partition and between-mode statistical modeling strategy (CPPBM) is proposed for online monitoring of multimode multiphase batch processes. First, the time-varying characteristics of batch processes are concurrently analyzed across modes so that multiple sequential phases are simultaneously identified for all modes. The feature is that both time-wise dynamics and mode-wise variations are considered to get the consistent phase boundaries. Then within each phase, between-mode statistical analysis is performed where one mode is chosen for the development of reference monitoring system and the relative changes from the reference mode to each alternative mode are analyzed. From the between-mode perspective, each of the original reference monitoring subspaces, including systematic subspace and residual subspace, are further decomposed into two monitoring subspaces for each alternative mode, which reveal two kinds of between-mode relative variations. The part which shows significant increases represents the variations that will cause alarm signals if the reference models are used to monitor the alternative modes, whereas the part that shows no increases will not issue alarms. By modeling and monitoring different types of between-mode relative variations, the proposed CPPBM method can not only efficiently detect faults but also offer enhanced process understanding. It is illustrated with a typical multiphase batch process with multiple modes. (c) 2013 American Institute of Chemical Engineers AIChE J 60: 559-573, 2014