Artificial Neural Networks (ANN) are suitable in modeling high-dimensional and highly nonlinear elements, such as those found in the microwave arena. In modeling microwave components, the learning data is obtained from a detailed or “fine” model (typically an EM simulator), which is accurate but slow to evaluate. This is aggravated because simulations are needed for many combinations of input parameter values. This is the main drawback of conventional ANN modeling. We use available equivalent circuits or “coarse” models to overcome this limitation. In the Space Mapping (SM) based neuromodeling techniques an ANN is used to implement a suitable mapping from the fine to the coarse input space. The implicit knowledge in the coarse model not only allows us to decrease significantly the number of learning points needed, but also to reduce the complexity of the ANN and to improve the generalization performance. We present novel realizations of SM based neuromodels of practical passive components using commercial software. An SM-based neuromodel of a microstrip right angle bend is developed using NeuroModeler, and entered into HP ADS as a library component through an ADS plug-in module.Consejo Nacional de Ciencia y TecnologíaCom DevNSERC