In this talk we present a new regularized kernel based approach for the estimation of the second order moments of stationary stochastic processes. The correlation functions are assumed to be summable and estimated as the solution of a Tikhonov-type variational problem. The hypothesis space is a Reproducing kernel Hilbert space induced by the recently introduced Stable Spline kernel. In this way, the information on the decay to zero of the functions to be econstructed is incorporated in the estimation process. We show that the overall complexity of the proposed estimator scales linearly with the number of available samples of the processes. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters.