As　an　important　component　of　the　driver　assistance　system　or　autonomous　vehicle,　traffic-sign　recognition　can　provide　drivers　or　vehicles　with　safety　and　alert　information　about　the　road.　This　paper　proposes　a　new　method　for　the　task　of　traffic-sign　recognition　by　employing　extreme　learning　machine　(ELM)　whose　infrastructure　is　a　single-hidden-layer　feed-forward　network.　This　method　includes　two　stages:　One　is　the　training　stage　which　estimates　the　parameters　of　ELM　based　on　training　images　of　traffic　signs;　the　other　is　the　recognition　stage　which　identifies　each　test　image　by　using　the　trained　ELM.　Histogram-of-gradient　descriptors　are　used　as　features　in　this　proposed　method.　The　German　traffic　sign　recognition　benchmark　data　set　[1]　with　more　than　50000　images　of　German　road　signs　over　43　classes　is　used.　Experimental　results　have　shown　that　this　proposed　method　achieves　not　only　high　recognition　precision　but　also　extremely　low　computational　cost　in　terms　of　both　training　and　recognition　stages.　An　outstanding　balance　between　recognition　ratio　and　computational　speed　is　obtained.　©　2014　IEEE.