In　this　paper,　empirical　likelihood　inference　for　longitudinal　data　within　the　framework　of　partial　linear　regression　models　are　investigated.　The　proposed　procedures　take　into　consideration　the　correlation　within　groups　without　involving　direct　estimation　of　nuisance　parameters　in　the　correlation　matrix.　The　empirical　likelihood　method　is　used　to　estimate　the　regression　coefficients　and　the　baseline　function,　and　to　construct　confidence　intervals.　A　nonparametric　version　of　Wilk＇s　theorem　for　the　limiting　distribution　of　the　empirical　likelihood　ratio　is　derived.　Compared　with　methods　based　on　normal　approximations,　the　empirical　likelihood　does　not　require　consistent　estimators　for　the　asymptotic　variance　and　bias.　The　finite　sample　behaviour　of　the　proposed　method　is　evaluated　with　simulation　and　illustrated　with　an　AIDS　clinical　trial　data　set.