Depending　on　small　samples,　good　adaptation,　high　classification　accuracy,　are　important　to　remote　sensing　images　classification.　Grey　system　theory　studies　on　the　＂small　sample＂,　＂poor　information＂,　uncertain　systems,　which　are　difficult　for　Statistics　and　Probability　Theory,　fuzzy　mathematics.　The　paper　proposed　a　method,　named　Maximum　gray　slope　correlation　classification.　The　method　were　designed　and　implemented　based　on　the　gray　slope　correlation　degree　model.　Then,　the　comparative　classification　tests　between　the　gray　relational　classification　and　other　conventional　remote　sensing　classification　methods　were　implemented　using　small　samples.　The　classification　results　showed　that　the　accuracy　of　maximum　gray　slope　correlation　is　very　similar　to　spectral　angle　mapper,　and　close　to　the　support　vector　machine　and　neural　network.　The　classification　accuracies　were　sorted　as　following:　Support　Vector　Machines＞　Neural　Networks＞　maximum　gray　slope　correlation　＞　spectral　angle　mapper　＞　minimum　distance＞　maximum　likelihood＞　mahalanobis　distance.　Compared　with　other　classification　methods,　Maximum　gray　slope　correlation　classification　is　simple,　and　has　the　best　combined　accuracy　considering　every　subclass.