This paper presents a novel approach for the simultaneous modelling and forecasting of wind signal components. This is achieved in the complex domain by using novel neural network algorithms and architectures. We first perform a signal nonlinearity and component-dependent analyses, which suggest the use of modular complex-valued recurrent neural networks (RNNs). This RNN-based modelling rests upon a combination of nonlinearity, complexity and internal memory and allows for the multiple step ahead forecasting of the wind signal in its complex form (speed and direction). The approach is first verified on benchmark Data Set A (NH3 laser data) of the Santa Fe Time Series Prediction Competition together with artificial data generated by chaotic Mackey-Glass equations, and then applied to the real-world wind measurements. Simulations support the proposed architecture and algorithms. (c) 2005 Elsevier Ltd. All rights reserved.