Person　re-identification　is　one　of　the　most　critical　tasks　in　the　field　of　computer　vision　and　has　widely　applications　for　abnormal　detection　and　object　retrieval　in　video　surveillance.　In　this　paper,　we　give　an　extensive　comparison　for　different　kinds　of　visual　features　including　hand-craft　features　and　Convolutional　Neural　Networks　(CNN)　features.　We　run　the　experiments　on　three　public　dataset　CASIA,　Market1501　and　CUHK03.　Through　A　detail　comparison　and　analysis　on　different　features　with　different　similarity　measures,　we　find　Colorhistogram　and　ScalableColor　features　are　most　robust　to　occlusion　on　CASIA,　while　GoogleNet　and　VG-GNet　features　have　good　robustness　as　well.　For　all　single　features,　GoogleNet　feature　achieves　the　highest　results　on　Market1501　and　CUHK03.　For　feature　fustion,　GoogleNet　feature　with　ColorStructure　achieve　the　best　result　on　Market1501　and　GoogleNet　feature　wth　Colorhistogram　achieve　the　best　result　on　CUHK03.　For　similarity　measure,　Cosine　distance　is　evaluated　to　be　the　best　one　in　our　experiments.