Empirical-likelihood-based　inference　for　the　parameters　in　a　partially　linear　single-index　model　is　investigated.　Unlike　existing　empirical　likelihood　procedures　for　other　simpler　models,　if　there　is　no　bias　correction　the　limit　distribution　of　the　empirical　likelihood　ratio　cannot　be　asymptotically　tractable.　To　attack　this　difficulty　we　propose　a　bias　correction　to　achieve　the　standard　χ2-limit.　The　bias-corrected　empirical　likelihood　ratio　shares　some　of　the　desired　features　of　the　existing　least　squares　method:　the　estimation　of　the　parameters　is　not　needed;　when　estimating　nonparametric　functions　in　the　model,　undersmoothing　for　ensuring　√n-consistency　of　the　estimator　of　the　parameters　is　avoided;　the　bias-corrected　empirical　likelihood　is　self-scale　invariant　and　no　plug-in　estimator　for　the　limiting　variance　is　needed.　Furthermore,　since　the　index　is　of　norm　1,　we　use　this　constraint　as　information　to　increase　the　accuracy　of　the　confidence　regions　(smaller　regions　at　the　same　nominal　level).　As　a　by-product,　our　approach　of　using　bias　correction　may　also　shed　light　on　nonparametric　estimation　in　model　checking　for　other　semiparametric　regression　models.　A　simulation　study　is　carried　out　to　assess　the　performance　of　the　bias-corrected　empirical　likelihood.　An　application　to　a　real　data　set　is　illustrated.　©　2006　Royal　Statistical　Society.