This　paper　proposes　a　hierachical　method　for　traffic　sign　detection　by　employing　extreme　learning　machine　(ELM)　whose　infrastructure　is　a　single-hidden-layer　feed-forward　network.　This　proposed　method　consists　of　three　modules:　Coarse　detection　module,　fine　detection　module　and　candidates　clustering　module.　Histogram　of　oriented　gradient　(HOG)　and　color　histogram　are　used　as　features　of　signs.　This　proposed　method　is　tested　on　German　traffic　sign　detection　benchmark　(GTSDB)　data　set,　which　has　more　than　900　images　of　German　road　signs　covering　43　classes.　The　architecture　of　this　proposed　method　is　simple　and　it　has　strong　extensionality.　Experimental　results　have　shown　that　this　proposed　method　achieves　98.60%　in　terms　of　area　under　curve　(AUC)　for　all　categories　of　traffic　signs　in　the　dataset.