Tool　wear　is　unavoidable　during　machining,　which　is　one　of　the　most　common　tool　failure　modes.　It　is　significant　to　evaluate　the　tool　state　quickly　and　effectively　for　timely　tool　change　strategy.　The　cutting　vibration　signals　after　tool　wear　show　strong　non-Gaussian　characteristics.　Higher　order　spectrum　is　a　powerful　tool　for　analyzing　the　non-Gaussian　characteristics　of　signals,　and　can　restrain　noise　and　provide　more　information　than　classical　power　spectrum　analysis.　This　paper　presents　a　milling　tool　wear　state　monitoring　method　based　on　higher　order　spectrum　entropy.　Due　to　the　large　amount　of　calculation　of　bispectrum,　bispectrum　diagonal　slice　is　investigated.　And　the　diagonal　slice　spectral　entropy　is　proposed　as　tool　wear　indicator　to　monitor　tool　state.　To　verify　the　proposed　method,　cutting　vibration　signal　of　CNC　machining　center　were　collected　and　analyzed.　The　experimental　results　showed　that　the　proposed　approach　can　effectively　monitor　and　diagnose　the　tool　state,　and　has　good　robustness.　It　is　feasible　and　effective　for　online　monitoring　milling　tool　wear.