Unnatural　control　chart　patterns　(CCPs)　usually　correspond　to　the　specific　factors　in　a　manufacturing　process,　so　the　control　charts　have　become　important　means　of　the　statistical　process　control.　Therefore,　an　accurate　and　automatic　control　chart　pattern　recognition　(CCPR)　is　of　great　significance　for　manufacturing　enterprises.　In　order　to　improve　the　CCPR　accuracy,　experts　have　designed　various　complex　features,　which　undoubtedly　increases　the　workload　and　difficulty　of　the　quality　control.　To　solve　these　problems,　a　CCPR　method　based　on　a　one-dimensional　convolutional　neural　network　(1D-CNN)　is　proposed.　The　proposed　method　does　not　require　to　extract　complex　features　manually;　instead,　it　uses　a　1D-CNN　to　obtain　the　optimal　feature　set　from　the　raw　data　of　the　CCPs　through　the　feature　learning　and　completes　the　CCPR.　The　dataset　for　training　and　validation,　containing　six　typical　CCPs,　is　generated　by　the　Monte-Carlo　simulation.　Then,　the　influence　of　the　network　structural　parameters　and　activation　functions　on　the　recognition　performance　is　analyzed　and　discussed,　and　some　suggestions　for　parameter　selection　are　given.　Finally,　the　performance　of　the　proposed　method　is　compared　with　that　of　the　traditional　multi-layer　perceptron　method　using　the　same　dataset.　The　comparison　results　show　that　the　proposed　1D-CNN　method　has　obvious　advantages　in　the　CCPR　tasks.　Compared　with　the　related　literature,　the　features　extracted　by　the　1D-CNN　are　of　higher　quality.　Furthermore,　the　1D-CNN　trained　with　simulation　dataset　still　perform　well　in　recognizing　the　real　dataset　from　the　production　environment.