A　large　number　of　multivariate　statistical　methods　have　been　applied　to　process　monitoring,　but　conventional　methods　only　extract　limited　feature　information　that　often　cannot　effectively　monitor　the　quality　related　changes　of　production　characteristics　in　batch　processes.　In　order　to　improve　the　effect　of　the　process　monitoring,　this　paper　proposes　a　batch　process　monitoring　method　based　on　Multistage　Over-complete　Independent　Component　Analysis　(OICA)　algorithm.　Firstly,　the　Affinity　Propagation　algorithm　(AP)　is　used　to　divide　the　batch　production　process.　Secondly,　the　extra　quality　information　extracted　by　Partial　Least　Squares　(PLS)　algorithm　is　input　into　OICA　algorithm.　Finally,　a　monitoring　model　is　established　for　process　monitoring　in　each　sub-stage.　The　effectiveness　of　the　proposed　method　has　been　verified　by　comparing　with　the　conventional　methods　in　the　fed-batch　penicillin　fermentation　process.