In the framework of the electromagnetic approaches based on learning-by-example (LBE) techniques, this thesis focuses on the development of a strategy for the solution of complex problems by means of support vector machine (SVM). The proposed instance-based classification method compared to more traditional optimization techniques solves the arising quadratic optimization problem with constraints in a simple and reliable way leveraging on the statistical learning theory which enables the design of optimal classifiers with a solid theoretical framework. A set of input/output relations representing the training dataset permits to avoid the a-priori knowledge about the system. By exploiting the generalization capabilities, the robustness against noise and the real-time performance, this technique has been proven to be suitable for more than one real-world application. The investigated problems are addressed by integrating the measured electromagnetic field with a suitably defined classifier that is aimed at defining a real-time reconstruction of the observed domain. For each application field a set of numerical results have been reported in order to assess the effectiveness and flexibility of the proposed approach. The real-time capabilities as well as the feasibility when dealing with real data have been also verified by means of an experimental setup for the passive tracking of non-cooperative targets moving throughout the investigated area.