Radar images, range profiles and scattering centers are used as feature parameters in radar target classification applications. Scattering center parameters, when used as feature parameters, enable an efficient compression of feature space compared to classical target classification methods based on radar images and range profiles. A method used for the estimation of scattering centers via cancellation of side lobes is the CLEAN algorithm. In this work, model based Prony, MUSIC, ESPRIT and evolutionary based CLEAN methods are applied for the estimation of scattering centers. A hybrid method is proposed which improves the convergence of evolutionary based CLEAN. Scattering centers which are estimated by aforementioned methods are classified using correlation based matching score method, Bayes classifier and artificial neural networks. Classification is accomplished using simulated data of four different aircraft models created by the point target model at different frequency bands and aspect angles.