Mill　load　is　one　of　the　key　factors　that　affects　the　operation　optimization　and　control　of　grinding　process.　It　is　difficult　to　detect　accurately　in　real　time.　The　mill　load　relates　to　multiple　channel　mechanical　signals,　such　as　the　mill　shell　vibration　signals,　the　shaft　seat　front　and　rear　vibration　signals,　the　acoustic　signals　near　to　the　shell　surface　and　under　the　grinding　area,　etc.　How　to　evaluate　these　sub-band　feature　of　the　mechanical　spectrum　of　the　above　channels　is　a　difficult　problem.　It　can　help　to　clarify　the　ball　mill　grinding　mechanism　and　the　mechanical　signal　generation　mechanism.　To　solve　these　problems,　this　paper　proposes　a　multi-channel　mechanical　frequency　spectrum　sub-band　feature　evaluation　method　based　on　multiple　types　correlation　analysis.　Firstly,　the　multi-channel　mechanical　signals　are　transformed　from　time　domain　to　frequency　domain　to　obtain　the　frequency　spectrum　data,　which　are　divided　into　several　sub-bands　to　extract　features.　Then,　the　normalized　correlation　coefficients　between　the　multi-channel　sub-band　feature　and　the　mill　load　are　calculated.　Thirdly,　based　on　the　partial　least　square　algorithm,　the　variable　projection　importance　(VIP)　value　of　sub-band　features　are　also　calculated.　The　VIPs　are　normalized　same　as　that　of　correlation　coefficient.　Finally,　a　new　index　is　calculated　by　using　normalized　correlation　coefficient　and　VIP　value　and　the　model＇s　prediction　performance.　These　sub-band　features　of　single　channel　and　all　channel　mechanical　signals　are　measured　based　on　the　new　defined　combined　evaluation　index.　The　multi-channel　mechanical　signals　of　a　laboratory-scale　ball　mill　are　used　to　verify　the　effectiveness　of　the　proposed　method.　©　2020　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.