There　are　two　problems　that　affect　the　accuracy　of　defect　classification　for　automated　radiographic　NDT.　One　is　the　poor　generalisation　of　the　classification　method　led　by　a　small　training　sample　or　an　improper　classifier,　and　the　other　is　the　poor　separability　of　the　feature　group.　To　solve　the　former,　we　propose　a　method　based　on　the　direct　multiclass　support　vector　machine　(DMSVM)　to　classify　the　defect,　which　has　good　generalisation　under　the　circumstances　of　a　small　training　set.　To　tackle　the　latter,　we　suggest　four　new　features　(three　of　them　are　based　on　the　defect　region)　to　characterise　the　defect,　which　greatly　improve　the　separability　of　the　feature　group.　Three　classifiers　(one-versus-rest　SVM,　one-versus-one　SVM　and　MLP　neuron　network)　and　a　group　of　feathers　are　used　to　compare　with　the　classifier　and　the　feature　group　we　proposed.　The　bootstrap　estimate　is　used　to　estimate　their　performances.　The　eyperimental　results　demonstrate　that　the　bootstrap　accuracy　estimate　of　DMSVM　is　94.25%,　which　is　higher　than　that　achieved　by　the　three　compared　classifiers.　Moreover,　the　separability　of　the　suggested　feature　group　is　equivalent　to　that　of　the　counterpart　but　with　a　two-thirds　size,　and　the　computation　time　is　cut　by　22.17%.