An　operational　optimization　control　for　a　mineral　grinding　process　is　limited　by　unmeasured　load　parameter　inside　a　ball　mill　given　its　complex　and　unclear　production　mechanism.　A　mechanism　characteristic　analysis　and　soft　measuring　method　for　mill　load　parameter　based　on　mechanical　vibration　and　acoustic　signals　in　the　mineral　grinding　process　is　reviewed　in　this　study.　The　modeling　process　based　on　the　mechanical　vibration　and　acoustic　signals　for　the　mill　load　parameters　are　summarized　as　a　class　of　intelligent　selective　ensemble　modeling　problem.　The　applied　soft　measuring　strategies　for　mill　load　parameter　measurement　are　divided　into　three　types,　namely,　off-line　modeling,　online　modeling,　and　virtual　sample　generation,　followed　by　a　detailed　discussion.　Possible　directions　for　mill　load　soft　measurement　techniques　are　provided　for　future　research.　These　techniques　include　a　vibration　mechanism-based　multi-component　signal　analysis,　off-line　intelligent　ensemble　soft　measuring　model　based　on　simulation　operational　expert　cognitive　process,　online　updating　strategy　based　on　intelligently　identified　samples,　and　a　mill　load　status　intelligent　recognition　mode　based　on　reinforcement　learning　strategy.