The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification system is used: first, the correlation functions are estimated by the Random Decrement Technique, and then the system parameters are identified from the correlation function estimates. Three different techniques are used in the parameter identification process: a simple non-parametric method, estimation of an Auto Regressive (AR) model by solving an overdetermined set of Yule-Walker equations and finally, least-square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average (ARMA) model directly to the system output from a single-degree-of-freedom system loaded by white noise.