Color　information　is　an　important　indicator　of　color　matching.　It　is　recommended　to　use　hue　(H)　and　saturation　(S)　to　improve　the　accuracy　of　color　analysis.　The　proposed　method　for　dental　shade　matching　in　this　study　is　based　on　the　hue,　saturation,　value　(HSV)　color　model.　To　evaluate　the　performance　of　the　proposed　method　in　matching　dental　shades,　peak　signal-to-noise　ratio　(PSNR),　structural　similarity　index　(SSIM),　composite　peak　signal-to-noise　ratio　(CPSNR),　and　S-CIELAB　(Special　International　Commission　on　Illumination,　L*　for　lightness,　a*　from　green　to　red,　and　b*　from　blue　to　yellow)　were　utilized.　To　further　improve　the　performance　of　the　proposed　method,　dental　image　samples　were　multiplied　by　the　weighted　coefficients　derived　by　training　the　model　using　machine　learning　to　reduce　errors.　Thus,　the　PSNR　of　97.64%　was　enhanced　to　99.93%　when　applied　with　the　proposed　fuzzy　decision　model.　Results　show　that　the　proposed　method　based　on　the　new　fuzzy　decision　technology　is　effective　and　has　an　accuracy　of　99.78%,　which　is　a　significant　improvement　of　previous　results.　The　new　fuzzy　decision　is　a　method　that　combines　the　HSV　color　model,　PSNR(H),　PSNR(S),　and　SSIM　information,　which　are　used　for　the　first　time　in　research　on　tooth　color　matching.　Results　show　that　the　proposed　method　performs　better　than　previous　methods.　©　2020　M　Y　U　Scientific　Publishing　Division.　All　rights　reserved.