In this paper, we propose a copula ML estimation method for the sample selection modelusing the Ali-Mikhail-Haq (AMH) copula. The proposed AMH copula ML estimation iscompared with the well-known bivariate ML estimation and Heckman’s two-stepestimation. Monte Carlo experiments are conducted to compare their performance in termsof the mean squared error (MSE) depending on the following 2 conditions: (i) whether theimposed distributional assumption is correct, and (ii) whether some regressors of theparticipation and outcome equation are correlated. The results of the experiments show thatthe estimation results for the proposed method can be better than those of the two wellknownmethods, particularly when the imposed distributional assumption is incorrect andsome regressors of the two equations are correlated. Hence, the proposed method can be apractically useful alternative for the sample selection model.