Elman network is a class of recurrent neural networks used for function approximation. The main problem of this class is that its structure has a set of global sigmoid functions at its hidden layer. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This paper introduces a new version of the Elman network named Elman Recurrent Wavelet Neural Network (ERWNN). It merges the multiresolution property of the wavelets and the learning capabilities of the Elman neural network to inherit the advantages of the two paradigms and to avoid their drawbacks. Stability and convergence property is proven for the proposed network. The paper also develops a model reference control scheme using the proposed ERWNN. The proposed scheme belongs to indirect adaptive control schemes. The dynamic back propagation (DBP) algorithm is employed to train both the two networks structured for the indirect control scheme. This paper derives also the plant sensitivity for adjusting the parameters of the developed controller. The advantages of this new version of ERWNN in modeling and controlling time intensive dynamic processes, are reflected in our simulation results.