Vehicle　classification　plays　an　important　part　in　Intelligent　Transport　System.　Recently,　deep　learning　has　showed　outstanding　performance　in　image　classification.　However,　numerous　parameters　of　the　deep　network　need　to　be　optimized　which　is　time-consuming.　PCANet　is　a　light-weight　deep　learning　network　that　is　easy　to　train.　In　this　paper,　a　new　robust　vehicle　classification　method　is　proposed,　in　which　the　deep　features　of　PCANet,　handcrafted　features　of　HOG　(Histogram　of　Oriented　Gradient)　and　HU　moments　are　extracted　to　describe　the　content　property　of　vehicles.　In　addition,　the　spatial　location　information　is　introduced　to　HU　moments　to　improve　its　distinguishing　ability.　The　combined　features　are　input　to　SVM　(Support　Vector　Machine)　to　train　the　classification　model.　The　vehicles　are　classified　into　six　categories,　i.e.　large　bus,　car,　motorcycle,　minibus,　truck　and　van.　We　construct　a　VehicleDataset　including　13700　vehicle　images　extracted　from　real　surveillance　videos　to　carry　out　the　experiments.　The　average　classification　accuracy　can　achieve　98.34%,　which　is　4.49%　higher　than　that　obtained　from　the　conventional　methods　based　on　＂Feature　+　Classifier＂　and　is　also　slightly　higher　than　that　from　GoogLeNet　(98.26%).　The　proposed　method　doesn＇t　need　GPU　and　has　much　greater　convenience　than　GoogLeNet.　The　experimental　results　have　demonstrated　that　for　a　specific　task,　the　combination　of　the　deep　features　obtained　from　light-weight　deep　learning　network　and　the　handcrafted　features　can　achieve　comparable　or　even　higher　performance　compared　to　the　deeper　neural　network.