To　ensure　tool　life　and　control　the　rejects　rate　of　work-piece,　a　method　to　extract　clutter　induced　by　tool　wear　and　abnormal　vibration　in　spindle　current　was　proposed.　In　addition,　wear　identification　of　end　milling　cutter　was　realized　by　Convolutional　Neural　Network　(CNN).Based　on　the　results　of　irregular　clutter　components　in　spindle　current　caused　by　tool　wear　and　abnormal　vibration,　Fourier　series　fitting　was　used　to　decompose　current　into　harmonic　components　and　current　clutter.　Harmonic　components　reflected　the　quasi-static　change　in　the　current　RMS　and　current　clutter　reflected　the　cutting　edge　and　flank　wear　status　of　the　end　milling　cutter　and　abnormal　vibration.　The　current　clutter　signals　were　input　into　CNN　for　feature　extraction　and　classification　of　end　milling　cutter　status.　Experiments　results　showed　that　the　proposed　method　could　eliminate　the　influence　of　cutting　vibration　and　parameters　on　the　accuracy　of　tool　wear　status　identification,　realize　the　accurate　status　identification　of　the　end　milling　cutter　and　lay　the　foundation　for　predicting　the　remaining　life　of　the　end　milling　cutter　and　formulating　the　end　milling　cutter　changing　rules　scientifically　and　rationally　under　complex　conditions.　©　2021,　Editorial　Department　of　CIMS.　All　right　reserved.