Aiming　at　the　batch　process　that　has　the　non-Gaussian　distribution　or　mixed　distribution,　a　new　monitoring　method　based　on　modified　MICA-PCA　is　researched.　Process　information　of　non-Gaussian　is　first　extracted　using　the　MICA　method.　Setting　threshold　value　of　negative　entropy　is　used　to　automatically　select　the　independent　components,　which　can　overcome　the　shortcoming　of　predefining　the　number　of　independent　components　in　traditional　method　of　ICA.　The　confidence　limits　of　the　corresponding　monitoring　statistics　are　determined　using　kernel　density　estimation;　then　the　process　residual　information,　which　is　multivariate　Gaussian　distribution,　is　further　analyzed　and　processed　using　PCA.　The　method　is　applied　to　the　fermentation　process　monitoring　of　obtaining　interleukin　by　recombinant　Escherichia　coli,　in　a　biochemical　pharmaceutical　factory　in　Beijing.　Results　show　that　when　process　variables　are　not　Gaussian　distribution,　the　method　can　accurately　monitor　the　process　and　effectively　reduce　the　alarm　failure　and　false　alarm　of　traditional　method.