Object　grasping　using　vision　is　one　of　the　important　functions　of　manipulators.　Machine　learning　based　methods　have　been　proposed　for　grasp　detection.　However,　due　to　the　variety　of　grasps　and　3D　shapes　of　objects,　how　to　effectively　find　the　best　grasp　is　still　a　challenging　issue.　Thus　this　paper　presents　an　extreme　learning　machine　(ELM)　based　method　to　cope　with　this　issue.　This　proposed　method　consists　of　three　successive　modules,　including　candidate　object　detection,　estimation　of　object＇s　major　orientations　and　grasp　detection.　In　the　first　module,　candidate　object　region　is　extracted　based　on　depth　information.　In　the　second　module,　object＇s　major　orientations　guide　the　directions　for　sliding　windows.　In　the　third　module,　a　cascaded　classifier　is　trained　to　identify　the　right　grasp.　ELM　is　used　as　the　base　classifier　in　the　cascade.　Histograms　of　oriented　gradients　(HOG)　are　used　as　features.　Experimental　results　in　benchmark　dataset　and　real　manipulators　have　shown　that　this　proposed　method　outperforms　other　methods　in　terms　of　accuracy　and　computational　efficiency.