At the boundaries between photonics and dynamic systems theory, we combine recent advances in neural networks with opto-electronic nonlinearities to demonstrate a new way to perform optical information processing. The concept of reservoir computing arose recently as a powerful solution to the issue of training recurrent neural networks. Indeed, it is comparable to, or even outperforms, other state of the art solutions for tasks such as speech recognition or time series prediction. As it is based on a static topology, it allows making the most of very simple physical architectures having complex nonlinear dynamics. The method is inherently robust to noise and does not require explicit programming operations. It is therefore particularly well adapted for analog realizations. Among the various implementations of the concept that have been proposed, we focus on the field of optics. Our experimental reservoir computer is based on opto-electronic technology, and can be viewed as an intermediate step towards an all optical device. Our fiber optics system is based on a nonlinear feedback loop operating at the threshold of chaos. In its present preliminary stage it is already capable of complicated tasks like modeling nonlinear systems with memory. Our aim is to demonstrate that such an analog reservoir can have performances comparable to state of the art digital implementations of Neural Networks. Furthermore, our system can in principle be operated at very high frequencies thanks to the high speed of photonic devices. Thus one could envisage targeting applications such as online information processing in broadband telecommunications.© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.