We consider the nonparametric estimation of the hazard function, using a recursive kernel estimator of the density and the distribution function. Assuming that the data proceed from a strong mixing stationary process, the strong consistency of the proposed estimator is obtained. The rate of convergence is the same as that in the independence case. Asymptotic normality of the estimator is also proven
