Tongue　inspection　is　an　essential　part　in　the　four　diagnostic　methods　in　traditional　Chinese　medicine　(TCM).　Subject　to　the　variation　in　conditions　such　as　the　imperfection　of　capturing　environment,　illumination　and　imaging　devices,　the　captured　tongue　images　usually　contain　certain　color　distortion　compared　to　the　actual　tongue　images,　and　such　distortion　has　negative　impact　on　the　diagnosis　from　doctors.　Therefore,　this　paper　proposes　a　simulated　annealing　(SA)-genetic　algorithm　(GA)-back　propagation　(BP)　neural　network-based　color　correction　algorithm　for　TCM　tongue　images.　The　main　contributions　of　this　paper　include　two　aspects:　First,　not　all　of　the　color　samples　from　the　whole　color　gamut　are　used　to　train　the　color　correction　model,　only　a　number　of　colors　that　are　similar　to　those　of　the　tongue　body,　tongue　coating　and　skin　are　selected　from　the　entire　color　sample　set　and　used　for　the　color　correction,　which　will　greatly　reduce　the　computational　complexity　of　training　process　and　improve　the　correction　accuracy.　Second,　to　further　improve　the　correction　accuracy,　SA-GA-BP　neural　network　algorithm　is　utilized　in　training　process　to　establish　the　color　mapping　model,　with　the　captured　samples　of　such　color　checkers　under　the　capturing　environment　taken　as　the　input　data　and　the　standard　color　data　as　output.　As　to　the　problem　that　the　color　correction　models　obtained　by　using　the　SA-GA-BP　neural　network　method　is　not　unique,　the　optimal　color　mapping　model　is　selected　based　on　the　principle　of　minimizing　the　average　color　difference　between　the　output　values　of　test　samples　and　standard　colors.　Experimental　results　demonstrate　that　the　performance　of　color　correction　obtained　by　the　proposed　algorithm　is　superior　to　that　based　on　the　whole　color　gamut　color　correction　algorithm,　while　the　training　time　is　as　6.7%　low　as　that　of　the　whole　color　gamut　color　correction　algorithm.　(C)　2014　Elsevier　B.V.　All　rights　reserved.