This　study　aims　to　develop　a　methodology　to　describe　and　predict　the　statistical　characteristics　of　individual　ore　particles　in　terms　of　length,　width,　height,　volume,　mass,　area,　circularity,　aspect　ratio,　density,　and　porosity.　The　mean　value,　standard　deviation,　and　appropriate　distribution　function　were　calculated　or　identified　for　each　data　set　of　a　given　particle　property　in　a　given　size　fraction.　It　was　found　that　the　mean　value　and　the　standard　deviation　of　the　same　particle　property　can　either　be　predicted　from　particle　size　or　be　approximated　by　a　constant.　The　best-fit　distribution　of　each　kind　of　particle　property　was　identified　by　the　Anderson-Darling　test　using　Minitab　software.　Generally,　the　data　sets　with　the　same　particle　property　but　different　size　fractions　and　ore　types　follow　the　same　distribution.　A　methodology　was　developed　to　predict　the　distribution　of　individual　particle　properties　in　a　given　size　fraction　by　particle　size,　and　the　fitting　quality　is　good　in　most　cases.　The　statistical　characteristics　of　individual　ore　particles　can　improve　the　precise　processing　of　ore　feed　in　concentrators,　the　preparation　of　feed　samples　for　lab-scale　testing,　the　calibration　of　image　analysis　of　ore　particle　size　distribution,　etc.