This　paper　proposes　a　computationally　efficient　method　for　traffic　sign　recognition　(TSR).　This　proposed　method　consists　of　two　modules:　1)　extraction　of　histogram　of　oriented　gradient　variant　(HOGv)　feature　and　2)　a　single　classifier　trained　by　extreme　learning　machine　(ELM)　algorithm.　The　presented　HOGv　feature　keeps　a　good　balance　between　redundancy　and　local　details　such　that　it　can　represent　distinctive　shapes　better.　The　classifier　is　a　single-hidden-layer　feedforward　network.　Based　on　ELM　algorithm,　the　connection　between　input　and　hidden　layers　realizes　the　random　feature　mapping　while　only　the　weights　between　hidden　and　output　layers　are　trained.　As　a　result,　layer-by-layer　tuning　is　not　required.　Meanwhile,　the　norm　of　output　weights　is　included　in　the　cost　function.　Therefore,　the　ELM-based　classifier　can　achieve　an　optimal　and　generalized　solution　for　multiclass　TSR.　Furthermore,　it　can　balance　the　recognition　accuracy　and　computational　cost.　Three　datasets,　including　the　German　TSR　benchmark　dataset,　the　Belgium　traffic　sign　classification　dataset　and　the　revised　mapping　and　assessing　the　state　of　traffic　infrastructure　(revised　MASTIF)　dataset,　are　used　to　evaluate　this　proposed　method.　Experimental　results　have　shown　that　this　proposed　method　obtains　not　only　high　recognition　accuracy　but　also　extremely　high　computational　efficiency　in　both　training　and　recognition　processes　in　these　three　datasets.