Online　monitoring　load　parameters　inside　the　ball　mill　is　the　key　to　improving　the　production　quality　and　quantity　of　the　mineral　grinding　process.　In　this　paper,　the　experimental　analysis　of　wet　mill　load　parameter　(MLPs)　based　on　multiple　channel　mechanical　signals　is　presented.　A　series　of　experiments　is　conducted　to　investigate　the　mechanical　frequency　spectrum　characteristics　in　terms　of　different　grinding　conditions,　such　as　only-ball,　-mineral,　or　-water　load　change.　Based　on　power　spectra　density　(PowSD),　multiple　channel　mechanical　signals　are　interpreted　for　different　MLPs,　i.e.,　mineral-to-ball　volume　ratio　(MBVR),　pulp　density　(PD),　and　charge　volume　ratio　(CVR),　in　detail.　Experimental　results　show　that　the　PowSDs　of　these　mechanical　signals　are　positively　correlated　with　CVR　and　negatively　correlated　with　MBVR　and　PD.　Further,　the　generation　mechanism　of　these　mechanical　signals　is　qualitatively　analyzed,　and　a　new　measurement　method　for　the　contribution　rate　of　multiple　channel　mechanical　signals,　i.e.,　combination　estimation　index,　is　proposed.　The　results　show　the　different　contribution　rates　of　these　signals　to　various　MLPs　under　varied　grinding　conditions.　Appropriate　mechanical　channels　for　different　MLPs　must　be　selected　to　construct　an　effective　MLP　forecasting　model.