As　a　nonparametric　classification　algorithm,　K-Nearest　Neighbor　(KNN)　is　very　efficient　and　can　be　easily　realized.　However,　the　traditional　KNN　suggests　that　the　contributions　of　all　K　nearest　neighbors　are　equal,　which　makes　it　easy　to　be　disturbed　by　noises.　Meanwhile,　for　large　data　sets,　the　computational　demands　for　classifying　patterns　using　KNN　can　be　prohibitive.　In　this　paper,　a　new　Template　reduction　KNN　algorithm　based　on　Weighted　distance　(TWKNN)　is　proposed.　Firstly,　the　points　that　are　far　away　from　the　classification　boundary　are　dropped　by　the　template　reduction　technique.　Then,　in　the　process　of　classification,　the　K　nearest　neighbors＇　weights　of　the　test　sample　are　set　according　to　the　Euclidean　distance　metric,　which　can　enhance　the　robustness　of　the　algorithm.　Experimental　results　show　that　the　proposed　approach　effectively　reduces　the　number　of　training　samples　while　maintaining　the　same　level　of　classification　accuracy　as　the　traditional　KNN.