Many real-world processes tend to be chaotic and are not amenable to satisfactory analytical models. It has been shown here that for such chaotic processes represented through short chaotic noisy observed data, a multi-input and multi-output recurrent neural network can be built which is capable of capturing the process trends and predicting the behaviour for any given starting condition. It is further shown that this capability can be achieved by the recurrent neural network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the bifurcation diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control, etc. (c) 2005 Elsevier Ltd. All rights reserved.