Non-deterministic problems can be adequately characterized by identifying dynamic models that can represent them. Early in the study, a comparative analysis of the range, equivalence and adequacy of the models is initially performed. Types of models considered in this work are ARMA, transfer function and State models of discrete parameter, not necessarilly scalar or invariant. The usual identification methods are then succinttly examined and compared. The estimation problem of stochastic processes, using the innovation processes, using the innovation process approach, is also analyzed, with a view to a gradual development of concepts as regards the determination of the model structure. Recursive estimation algorithms (Kalman and others) are then compared, and the problem of finitely recursive properties and convergence is examined. Identification thecniques usually leod to more than one model capable of characterizing the stochastic process. The problem of choosing between these models is formulated as a hypothesis testing problem, to which the Maximum Likelyhood thecnique is applied. Test resolution follows immediately from the innovation process, and can indistinctly be applied to ARMA, Transfer Function or state models. In the case of ARMA and Transfer Function models, an even more simplified result can be obtained. The application of hypothesis testing to the non-Gausian assumption is also brought to focus.