Feature　extraction　is　the　key　issue　in　a　recognition　system.　Principal　Component　Analysis　(PCA)　is　one　of　the　most　widely　used　feature　extraction　algorithms.　But　it　is　inadequate　for　this　linear　method　to　describe　real　images　which　contain　complex　nonlinear　variations,　such　as　illumination,　distortion　and　so　on.　In　this　paper,　an　efficient　object　recognition　method　based　on　distance　Kernel　PCA　(KPCA)　is　proposed.　First,　a　new　kernel　called　distance　kernel　is　presented　to　set　up　the　corresponding　relation　between　the　higher-dimensional　feature　space　and　the　original　input　space.　Then,　PCA　was　performed　in　the　higher-dimensional　space　and　a　nearest　neighbor　strategy　was　used　for　decision-making.　The　experiments　on　both　ORL　face　database　and　general　object　image　dataset　collected　by　the　robot　camera　illustrate　that　KPCA　with　the　distance　kernel　outperforms　PCA　in　robot　object　recognition:　higher　recognition　accuracy　and　less　computing　time.　©　2010　IEEE.