Control　charts　are　an　important　tool　for　statistical　process　control　(SPC).　SPC　has　the　characteristics　of　fluctuation　and　asymmetry　in　the　symmetrical　coordinate　system.　It　is　a　graph　with　control　limits　used　to　analyze　and　judge　whether　the　process　is　in　a　stable　state.　Its　fast　and　accurate　identification　is　of　great　significance　to　the　actual　production.　The　existing　control　chart　pattern　recognition　(CCPR)　method　can　only　recognize　a　control　chart　with　fixed　window　size,　but　cannot　adjust　with　different　window　sizes　according　to　the　actual　production　needs.　In　order　to　solve　these　problems　and　improve　the　quality　management　effect　in　the　manufacturing　process,　a　new　CCPR　method　is　proposed　based　on　convolutional　neural　network　(CNN)　and　information　fusion.　After　undergoing　feature　learning,　CNN　is　used　to　extract　the　best　feature　set　from　the　control　chart,　while　at　the　same　time,　expert　features　(including　one　shape　features　and　four　statistical　features)　are　fused　to　complete　the　CCPR.　In　this　paper,　the　control　charts　of　10　different　window　sizes　are　generated　by　the　Monte　Carlo　simulation　method,　and　various　data　patterns　are　drawn　into　images,　then　the　CCPR　model　is　set　up.　Finally,　simulation　experiments　and　a　real　example　are　addressed　to　validate　its　feasibility　and　effectiveness.　The　results　of　simulation　experiments　demonstrate　that　the　recognition　method　based　on　CNN　can　be　used　for　pattern　recognition　for　different　window　size　control　charts,　and　its　recognition　accuracy　is　higher　than　the　traditional　ones.　In　addition,　the　recognition　method　based　on　information　fusion　performs　much　better.　The　result　of　a　real　example　shows　that　the　method　has　potential　application　in　solving　the　pattern　recognition　problem　of　control　charts　with　different　window　sizes.