In　industrial　manufacturing,　most　fermentation　processes　are　inherently　multiphase　and　uneven-length　batch　processes　in　nature.　Based　on　different　dynamic　nonlinear　characteristics　of　different　fermentation　phases,　a　new　strategy　is　proposed　by　using　multi-phase　dynamic　principal　component　analysis　(PCA)　for　fermentation　process　monitoring.　Using　Gaussian　mixture　model　(GMM)　clustering　arithmetic,　fermentation　process　data　are　divided　into　several　operation　stages,　since　GMM　is　adopted　to　discriminate　different　operation　modes.　Then,　run-to-run　variations　among　different　instances　of　a　phase　are　synchronized　by　using　dynamic　time　warping　(DTW),　and　sub-phase　dynamic　PCA　models　are　developed　for　every　phase.　Finally,　the　proposed　method　is　applied　to　monitor　both　the　industrial　processes　of　fed-batch　penicillin　production　and　interleukin-2　production　in　recombinant　E.　coli.　Results　demonstrate　that　fewer　false　alarms　and　small　fault　detection　delay　are　obtained　and　the　algorithm　is　proved　to　be　efficient.