With　the　automation　development　of　manufacturing　processes,　artificial　intelligence　technology　has　been　gradually　employed　to　increase　the　automation　and　intelligence　degree　in　quality　control　using　statistical　process　control　(SPC)　method.　In　this　paper,　an　SPC　method　based　on　a　fuzzy　adaptive　resonance　theory　(ART)　neural　network　is　presented.　The　fuzzy　ART　neural　network　is　applied　to　recognize　the　special　disturbance　of　the　manufacturing　processes　based　on　the　classification　on　the　histograms,　which　shows　that　the　fuzzy　ART　neural　network　can　adaptively　learn　the　features　of　the　histograms　of　the　quality　parameters　in　manufacturing　processes.　As　a　result,　the　special　disturbance　can　be　automatically　detected　when　a　feature　of　the　special　disturbance　starts　to　appear　in　the　histograms.　At　the　same　time,　combined　with　spectrum　analysis　of　the　autoregressive　model　of　quality　parameters,　the　fuzzy　ART　neural　network　can　also　be　utilized　to　adaptively　detect　the　abnormal　patterns　in　the　control　chart.　©　2010　Higher　Education　Press　and　Springer-Verlag　Berlin　Heidelberg.