In　evaluation　of　robotic　kinematic　and　dynamic　dexterity　performance,　there　are　many　different　indexes　to　express　robotic　kinematic　and　dynamic　dexterity,　the　global　performance　index　is　one　of　the　important　evaluation　index,　including　acceleration,　angular　acceleration　and　linear　acceleration　performance　index.　Those　different　indexes　tend　to　have　different　degrees　of　correlations　among　them.　Some　correlations　are　very　remarkable,　therefore,　the　provided　information　may　be　overlapped.　This　paper　describes　comprehensive　evaluation　of　global　performance　of　the　PUMA560　robot　with　different　scales　by　using　principal　component　analysis　(PCA)　and　kernel　principal　component　analysis　(KPCA),　which　is　characterized　by　linear　dimension　reduction　and　nonlinear　dimension　reduction　principle,　then　the　best　robotic　scales　with　comprehensive　global　performance　can　be　selected.　Results　show　that　KPCA　method　has　more　effective　reduction　effects,　and　can　reveal　the　nonlinear　relationship　among　different　single　performance　indexes　to　provide　more　comprehensive　global　performance　evaluation　information,　which　can　reveal　the　numerical　calculation　retationships　among　comprehensive　global　performance　and　scales,　and　provide　scientific　reference　for　selection　of　the　best　robotic　scales　based　on　comprehensive　global　performance　indexes.　©,　2014,　Beijing　University　of　Technology.　All　right　reserved.