Asynchronous machines are a topic of great interest in the research area of actuators. Due to the complexity of these systems and to the required performance, the modelling and control of asynchronous machines are complex questions. Problems arise when the control goals require accurate descriptions of the electric machine or when we need to identify some electrical parameters; in the models employed it becomes very hard to take into account all the phenomena involved and therefore to make the error amplitude adequately small. Moreover, it is well known that, though an efficient control strategy requires knowledge of the flux vector, direct measurement of this quantity, using ad hoc transducers, does not represent a suitable approach, because it results in expensive machines. It is therefore necessary to perform an estimation of this vector, based on adequate dynamic non-linear models. Several different strategies have been proposed in literature to solve the items in a suitable manner. In this paper the authors propose a neural approach both to derive NARMAX models for asynchronous machines and to design non-linear observers: the need to use complex models that may be inefficient for control aims is therefore avoided. The results obtained with the strategy proposed were compared with simulations obtained by considering a classical fifth-order non-linear model.