Batch　processes　are　very　important　in　most　industries　and　are　used　to　produce　high-value-added　products,　which　cause　their　monitoring　and　control　to　emerge　as　essential　techniques.　In　this　paper,　a　new　method　was　developed　based　on　Kalman　filter(KF)　and　multiway　kernel　principal　component　analysis(MKPCA)　for　on-line　batch　process　monitoring.　Three-way　batch　data　of　normal　batch　process　are　unfolded　batch-wise.　Then　KPCA　is　used　to　capture　the　nonlinear　characteristics　within　normal　batch　processes　and　set　up　the　more　accurate　monitoring　model　of　batch　processes.　The　on-line　monitoring　uses　a　Kalman　filter　which　can　estimate　the　entire　trajectory　of　the　current　batch　run.　Comparison　of　the　monitoring　performance　of　the　method　with　that　of　the　traditional　multiway　principal　component　analysis(MPCA)　method　on　a　benchmark　fed-batch　penicillin　fermentation　process　shows　that　the　proposed　method　had　better　monitoring　performance,　and　that　fewer　false　alarms　and　small　fault　detection　delay　were　obtained.　In　both　off-line　analysis　and　on-line　batch　monitoring,　the　proposed　approach　can　effectively　extract　the　nonlinear　relationships　among　the　process　variables.