Vehicle　classification　plays　an　important　role　in　intelligent　transport　system.　However,　because　the　conventional　vehicle　classification　methods　are　not　robust　to　variations　such　as　illumination,　weather,　noise,　and　the　classification　accuracy　cannot　meet　the　requirements　of　practical　applications.　Therefore,　a　new　vehicle　classification　method　using　Convolutional　Neural　Networks　is　proposed　in　this　paper,　which　consists　of　two　steps:　pre-training　and　fine-tuning.　In　pre-training,　GoogLeNet　is　pre-trained　on　ILSVRC-2012　dataset　to　obtain　the　initial　model　with　the　corresponding　connection　weights.　In　fine-tuning,　the　initial　model　is　further　fine-tuned　on　VehicleDataset　which　is　constructed　with　13,700　images　in　this　paper　to　obtain　the　final　classification　model.　All　images　in　the　VehicleDataset　are　extracted　from　real　highway　surveillance　videos,　including　variations　of　illumination,　noise,　resolution,　angle　of　video　cameras　and　weather.　The　vehicles　are　divided　into　six　categories,　i.e.,　bus,　car,　motorcycle,　minibus,　truck　and　van.　The　performance　evaluation　is　carried　out　on　the　VehicleDataset.　The　experimental　results　show　that　the　proposed　method　can　avoid　the　complicated　process　of　manually　extracting　features　and　the　average　classification　accuracy　is　up　to　98.26%,　which　is　3.42%　higher　than　the　conventional　methods　using　＂Feature　+　Classifier＂.