A　series　of　quality　control　problems　arise　in　multi-variety　and　small　batch　production　due　to　the　lack　of　data　and　other　factors.　In　this　paper,　an　integrated　model　in　which　control　chart　pattern　(CCP)　recognition　is　applied　for　anomaly　detection　is　proposed　to　solve　the　problems.　The　integrated　model　is　made　up　of　four　parts:　feature　extraction　module,　feature　selection　module,　classifier　module　and　anomaly　diagnosis　module.　In　the　first　module,　thirteen　shape　features　and　eight　statistical　features　of　control　charts　are　extracted.　In　the　second　module,　the　most　representative　feature　set　is　selected　by　the　sequential　floating　forward　selection　(SFFS)　method.　In　the　third　module,　a　multiclass　support　vector　machine　(MSVM)　which　is　optimized　by　beetle　antennae　search　(BAS)　algorithm　is　used　to　identify　abnormal　CCPs.　In　the　last　module,　the　results　of　pattern　recognition　are　utilized　to　analyze　the　possible　causes.　The　simulation　results　show　that　the　CCP　recognition　method　proposed　in　this　paper　has　higher　classification　accuracy　than　other　competing　methods　in　the　case　of　small　sample　with　small　amount　of　data.　Finally,　an　example　verifies　that　the　proposed　anomaly　detection　method　is　effective　in　multi-variety　and　small　batch　manufacturing　environment.　©　2020　IOP　Publishing　Ltd.　All　rights　reserved.