In this paper a computer-aided design (CAD) approach based on artificial neural networks (ANNs) was successfully introduced to determine the characteristic parameters of shielded multilayered coplanar waveguides (SMCPWs). ANNs are trained with four learning algorithms to obtain better performance and faster convergence with simpler structure. The best results for training and test were obtained from the models trained with Bayesian regularization and Levenberg-Marquardt algorithms. The neural model results are in very good agreement with the results available in the literature for SMCPWs and three other different shielded CPW structures. One can calculate the quasi-static parameters of these four different shielded CPW configurations using only one neural model proposed in this work, easily, simply and accurately.