A new EU Bathing Water Directive was implemented in March 2006, which sets a series of stringent microbiological standards. One of the main requirements of the new Directive is to provide the public with information on conditions likely to lead to short-term coastal pollution. The paper describes how numerical models have been combined with Artificial Neural Networks (ANNs) to develop an accurate and rapid tool for assessing the bathing water status of the Ribble Estuary, UK. Faecal coliform was used as the water quality indicator. In order to provide enough data for training and testing the neural networks, a calibrated hydrodynamic and water quality model was run for various river flow and tidal conditions. In developing the neural network model a novel data analysis tool called WinGamma was used in the model identification process. WinGamma is capable of determining the data noise level, even with the underlying function unknown, and whether or not a smooth model can be developed. Model predictions based on this technique show a good generalisation ability of the neural networks. Details are given of a series of experiments being undertaken to test the ANN model performance for different numbers of input parameters. The main focus has been to quantify the impact of including time series inputs of faecal coliform on the neural network performance. The response time of the receiving water quality to the river boundary conditions, obtained from the hydrodynamic model, has been shown to provide valuable knowledge for developing accurate and efficient neural networks.