The　sensitivity　of　various　features　that　are　characteristics　of　machine　performance　may　vary　significantly　under　different　working　conditions.　Thus　it　is　critical　to　devise　a　systematic　feature　extraction　(FE)　approach　that　provides　a　useful　and　automatic　guidance　on　using　the　most　effective　features　for　machine　performance　recognition　without　human　intervention.　This　paper　proposes　a　locality　preserving　projection　(LPP)-based　FE　approach　for　machine　performance　degradation　recognition.　Different　from　principal　component　analysis　(PCA)　that　aims　to　discover　the　global　structure　of　the　Euclidean　space,　LPP　is　capable　to　discover　local　structure　of　the　data　manifold.　This　may　enable　LPP　to　find　more　meaningful　low-dimensional　information　hidden　in　the　high-dimensional　observations　compared　with　PCA.　This　experimental　result　on　a　bearing　test-bed　shows　that　LPP-based　FE　improves　the　performance　of　recognizers　for　identifying　performance　degradation　of　bearings.