Generalized　linear　measurement　error　models,　such　as　Gaussian　regression,　Poisson　regression　and　logistic　regression,　are　considered.　To　eliminate　the　effects　of　measurement　error　on　parameter　estimation,　a　corrected　empirical　likelihood　method　is　proposed　to　make　statistical　inference　for　a　class　of　generalized　linear　measurement　error　models　based　on　the　moment　identities　of　the　corrected　score　function.　The　asymptotic　distribution　of　the　empirical　log-likelihood　ratio　for　the　regression　parameter　is　proved　to　be　a　Chi-squared　distribution　under　some　regularity　conditions.　The　corresponding　maximum　empirical　likelihood　estimator　of　the　regression　parameter　pi　is　derived,　and　the　asymptotic　normality　is　shown.　Furthermore,　we　consider　the　construction　of　the　confidence　intervals　for　one　component　of　the　regression　parameter　by　using　the　partial　profile　empirical　likelihood.　Simulation　studies　are　conducted　to　assess　the　finite　sample　performance.　A　real　data　set　from　the　ACTG　175　study　is　used　for　illustrating　the　proposed　method.