Feature　extraction,　as　one　of　the　two　important　components　in　handwritten　digit　recognition　systems,　is　still　a　key　research　area.　Principal　Component　Analysis　(PCA)　is　an　efficient　linear　feature　extraction　algorithm　and　is　widely　used　in　handwritten　digit　recognition　system.　However,　it　can　hardly　deal　with　the　pattern　with　complex　nonlinear　variations,　such　as　the　writing　interrupt,　noise　pollution　and　so　on.　This　paper　proposes　an　efficient　handwritten　digit　recognition　method　based　on　distance　Kernel　PCA　(KPCA).　First,　the　initial　input　data　is　mapped　into　a　higher-dimensional　space　with　the　distance　kernel　and　describes　the　whole　features　as　much　as　possible.　Then,　PCA　method　is　used　to　extract　the　Principal　Component　from　the　kernel　matrix.　Last,　SVM　acts　as　the　classifier　to　make　decision.　To　test　and　evaluate　the　proposed　method　performance,　a　series　of　studies　has　been　conducted　on　the　MINST　database.　Compared　with　the　other　models,　the　approach　proposed　shows　a　better　recognition　rate　and　is　more　satisfying.