The　congruent　matching　cells　(CMC)　method　was　invented　at　the　National　Institute　of　Standards　and　Technology　(NIST)　in　2012　for　automatic　and　objective　firearm　evidence　identifications　and　estimation　of　the　weight　of　evidence　in　firearm　evidence　identifications.　Since　2013,　five　CMC　algorithms　have　been　developed　at　NIST.　In　this　paper,　the　virtual　image　standard　(VIS)　is　proposed　through　trimming　and　stitching　KNM　images　for　quantitative　performance　evaluations　of　different　CMC　algorithms.　The　evaluation　criteria　include　the　correlation　accuracy　(both　the　CMC　numbers　and　distribution　pattern),　correlation　efficiency,　false　positive　(FP)　error　rate,　and　the　maximum　separation　of　known　matching　(KM)　and　known　non-matching　(KNM)　image　pairs.　The　VIS　composes　correlation　cells　from　different　KNM　images,　which　can　provide　a　ground　truth　for　verifying　the　CMC　numbers,　distribution　patterns,　and　FP　errors.　By　identifying　three　groups　of　VIS,　the　Convergence　CMC　algorithm　showed　superior　performances　for　the　future　casework　in　firearm　evidence　identifications.　Lastly,　the　success　of　this　study　suggests　that　the　VIS　could　also　be　used　to　optimize　the　correlation　parameters,　to　develop　and　test　new　CMC　algorithms,　and　evaluate　the　performance　before　it　is　put　into　use　for　firearm　examiner＇s　casework.