An adaptive control technique for nonlinear stable plants with unmeasurable state is presented. It is based on a recurrent neural network employed as a dynamical model of the plant. Using this dynamical model, a feedback linearizing control is computed and applied to the plant. Parameters of the model are updated on-line to allow for partially unknown and time-varying plant. The stability of the scheme is shown theoretically, and its performance and limitations of the assumptions are illustrated in simulations. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control.