With　the　development　of　stone　processing　and　sales,　effective　stone　surface　texture　image　recognition　methods　are　needed.　We　proposed　a　new　stone　surface　texture　image　recognition　method　based　on　texture　and　colour.　We　combine　the　following　visual　features:　Gabor　features　which　can　well　simulate　the　single　cell　sensing　profile　of　mammalian　visual　neurons,　The　Grey-level　Co-occurrence　Matrices(GLCM)　which　describe　image　gray　distribution　characteristics　and　spatial　location　information,　and　HSV　colour　features　which　are　consistent　with　human　visual　characteristics.　In　addition,　for　the　sub-image　of　the　stone　surface　texture　image　can　contain　its　original　image　texture　structure,　this　paper　adopts　the　block　training　idea,　subdividing　original　image　into　non-overlapping　sub-images　to　multiply　the　number　of　training　samples　for　SVM　classifier.　Extensive　experimental　results　show　that　the　proposed　method　has　a　reference　value　for　the　study　of　stone　texture　image　recognition.　©　2016　IEEE.