In this paper, we extend the composite quantile regression (CQR) method to a single-index model. The unknown link function is estimated by local composite quantile regression and the parametric index is estimated through the linear composite quantile. It is shown that the proposed estimators are consistent and asymptotically normal. The simulation studies and real data applications are conducted to illustrate the finite sample performance of the proposed methods.