Neural models based on the artificial neural networks (ANNs) for computing the effective permitivities and characteristic impedances of V-shaped conductor-backed coplanar waveguides are presented. The proposed neural models can also be used for calculating the characteristic parameters of conductor-backed coplanar waveguides. Six learning algorithms, Levenberg-Marguardt, bayesian regrdarization, quasi-Newton, scaled conjugate gradient, conjugate gradient of Fletcher-Reeves, and resilient propagation, are used to train the ANNs. The neural results are in very good agreement with the results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the ANNs trained by the LM algorithm. (c) 2007 Wiley Periodicals, Inc.