Aiming　at　the　complex　nonlinear　characteristic　and　slow　time-varying　behavior　of　batch　process,　a　new　method　was　developed　based　on　a　moving　window　MKPCA　(multi-way　kernel　principal　component　analysis)　for　on-line　batch　process　monitoring.　The　proposed　method　uses　a　moving　window　and　does　not　require　predicting　the　future　value　of　the　current　batch;　while　the　nonlinear　characteristics　within　normal　batch　processes　are　captured　by　using　KPCA.　It　also　enhances　the　reliability　of　the　monitoring　system　through　consecutively　updating　the　database　of　normal　batches.　The　proposed　method　is　used　to　evaluate　the　industrial　penicillin　fermentation　process　data　and　is　compared　with　traditional　MPCA　and　MKPCA　methods.　Results　show　that　the　proposed　method　has　better　performance,　can　effectively　extract　the　nonlinear　relationships　among　the　process　variables,　adapts　to　new　normal　operating　conditions　and　decrease　false　alarm　rate.