Person　re-identification　is　one　of　the　hot　topics　in　computer　vision.　How　to　design　a　robust　feature　representation　to　identify　pedestrians　is　a　key　problem　for　person　reidentification.　In　this　paper,　a　feature　representation　based　on　Multi-Statistics　Cascade　on　Pyramid　(MSCP)　is　proposed　for　person　re-identification.　The　MSCP　feature　is　composed　of　deep　PCA　network　feature　and　hand-crafted　features　of　Local　Maximal　Occurrence　(LOMO)　feature　and　color　correlogram.　MSCP　can　characterize　the　pedestrian　images　precisely　from　both　global　and　local　views.　The　Cross-view　Quadratic　Discriminant　Analysis　(XQDA)　is　employed　to　learn　the　distance　metric　of　MSCP　features.　And　then　a　novel　re-identification　method　based　on　MSCP　and　XQDA　is　achieved.　Experimental　results　on　VIPeR　Dataset　demonstrate　that　our　proposed　method　can　achieve　superior　identification　performance　compared　with　six　state-of-art　methods.　©　2016　IEEE.