Control　chart　is　a　useful　method　to　identify　process　problems　by　detecting　abnormal　pattern,　to　keep　process　in　control.　Aiming　at　the　problem　that　traditional　abnormal　patterns　were　fixed,　and　could　not　completely　identify　all　process　problems,　a　control　chart　recognition　based　on　transfer　learning　was　proposed.　Firstly,　six　kinds　of　abnormal　patterns　were　abstracted　from　the　characteristics　of　abnormal　processes.　Apply　Monte　Carlo　to　create　simulated　data,　and　process　data　to　reduce　the　effect　of　noise　by　standardizing　and　coding.　Control　chart　images　were　used　as　target　dataset　instead　of　numerical　data.　According　to　the　feature　based　transfer　learning　in　the　isomorphic　space,　apply　VGG16　network　model　to　extract　feature　to　improve　the　generalization　ability.　Finally,　the　output　of　the　feature　extraction　was　the　input　of　the　classifier　that　has　been　well　trained　on　the　target　dataset.　Control　chart　recognition　model　was　fine-tuned　according　to　the　recognition　results　during　the　process　of　training,　to　gain　the　optimal　one.　The　experimental　results　show　that　compared　with　BP　network　model,　the　accuracy　of　control　chart　recognition　network　model　based　on　transfer　learning　is　more　than　98%　with　the　less　sample　data.　©　2018　IEEE.