Key　performance　indicators　(KPI)-related　process　monitoring　has　been　of　great　significance　to　ensure　product　quality　and　economic　benefits　for　batch　processes.　Considering　that　different　phases　exhibit　different　characteristics,　one　of　the　key　issues　is　how　to　partition　the　whole　batch　process　into　different　phases　and　characterize　them　separately　by　multiple　phase　models.　In　order　to　model　and　monitor　batch　processes　more　accurately　and　efficiently,　a　novel　canonical　correlation　analysis　(CCA)　strategy　is　proposed　in　this　paper.　The　phase　partition　algorithm　is　designed　based　on　the　joint　canonical　variable　matrix　(JCVM).　Different　from　previous　methods,　it　considers　the　time　sequence　of　operation　phases　and　can　distinguish　the　phase　switches　from　dynamics　anomalies.　Using　this　algorithm,　phases　are　separated　in　order　from　a　KPI-related　perspective,　revealing　high　correlation　among　variables.　After　phase　partition,　a　novel　multi-phase　local　neighbourhood　standardization　CAA　(MPLNSCCA)　method　focusing　on　KPI　is　set　up　for　online　monitoring,　which　could　further　address　the　misclassification　problems.　The　advantages　of　the　proposed　method　are　illustrated　by　two　case　studies,　a　penicillin　simulation　platform　and　an　industrial　application　of　Escherichia　coli　fermentation,　respectively.