In this talk we consider an easy-to-use semiparametric method for analyzing matched case-control data when one of the covariates of interest is partially missing.Missing covariate information in matched case-control study may create bias and reduce efficiency of the parameter estimates.In order to cope with this situation we propose a robust approach which is comprised of estimating some functionals of the distribution of the partially missing covariate using a kernel regression technique in a conditional likelihood framework.The large sample properties of the proposed estimator are investigated and the asymptotic normality is obtained.A simulation study is carried out to assess the performance of the proposed method in terms of robustness and efficiency.