Aggregate　size　is　usually　measured　by　manual　sampling　and　sieving.　Machine　vision　techniques　can　provide　fast,　non-invasive　measurement.　However,　the　traditional　imaging　method　using　a　single　size　descriptor　to　discriminate　different　sieve-size　classes　of　coarse　aggregates　might　not　yield　high-precision　classification　results.　To　determine　the　optimum　supervised　machine　learning　model　for　coarse　aggregates　sieve-size　measurement,　17　methods　were　evaluated　and　compared.　To　train　our　model,　a　new　dataset　named　MFCA27　(Multiple　Features　of　Coarse　Aggregate　27)　was　introduced,　which　contains　27　features　of　aggregates　based　on　aggregate　threedimensional　(3D)　top-surface　object.　In　addition,　a　feature　selection　approach　for　investigating　how　accuracy　varied　with　the　datasets　under　different　feature　sets　was　developed,　where　feature　selection　was　performed　according　to　the　impurity-based　feature　importance　score　measured　using　an　extremely　randomized　tree　model.　Experiments　demonstrated　that　the　Gaussian　process　classifier　(GPC)　was　the　best-performing　method　on　the　datasets　with　two-　or　three-dimensional　(2D/3D)　feature　sets　in　terms　of　accuracy　and　robustness.　The　results　also　showed　that,　compared　with　the　traditional　aggregate　sieve-size　measurement　method,　which　is　based　on　a　single　size　descriptor,　GPC　can　achieve　an　accuracy　of　95.06%　on　the　test　dataset　of　MFCA27　in　the　aggregate　sieve-size　class　measurement　task.