The　empirical　likelihood　method　is　especially　useful　for　constructing　confidence　intervals　or　regions　of　the　parameter　of　interest.　This　method　has　been　extensively　applied　to　linear　regression　and　generalized　linear　regression　models.　In　this　paper,　the　empirical　likelihood　method　for　single-index　regression　models　is　studied.　An　estimated　empirical　log-likelihood　approach　to　construct　the　confidence　region　of　the　regression　parameter　is　developed.　An　adjusted　empirical　log-likelihood　ratio　is　proved　to　be　asymptotically　standard　chi-square.　A　simulation　study　indicates　that　compared　with　a　normal　approximation-based　approach,　the　proposed　method　described　herein　works　better　in　terms　of　coverage　probabilities　and　areas　(lengths)　of　confidence　regions　(intervals).　(c)　2005　Elsevier　Inc.　All　rights　reserved.