Abstract : In this paper, we study an estimation problem where the variables of interest are subject to both right censoring and measurement error. In this context, we propose a nonparametric estimation strategy of the hazard rate, based on a regression contrast minimized in a finite dimensional functional space generated by splines bases. We prove a risk bound of the estimator in term of integrated mean square error, discuss the rate of convergence when the dimension of the projection space is adequately chosen. Then, we define a data driven criterion of model selection and prove that the resulting estimator performs an adequate compromise. The method is illustrated via simulation experiments which prove that the strategy is successful.