Artificial neural networks (ANNs) have been applied to an increasing number of real-world problems of considerable complexity. Considered good pattern recognition engines, they offer ideal solutions to a variety of problems such as prediction and modelling where the industrial processes are highly complex. The present paper reports on the elaboration and the validation of a ‘software sensor’ using ANNs for online prediction of optimal coagulant dosage from raw water quality measurements, in a drinking water treatment plant. In the first part, the main parameters affecting the coagulant dosage are determined using a Principal Component Analysis. A brief description of this statistical study is given and experimental results are included. The second part of this work is dedicated to the development of a neural software sensor and the generation of an uncertainty indicator attached to the prediction. Bootstrap sampling has been used to generate a confidence interval for the model outputs. The ANN model was developed using the Levenberg-Marquardt method in combination with ‘weight decay’ regularization to avoid over-fitting. A linear regression model has also been developed for comparison with the ANN model. Experimental and performance results obtained from real data are presented and discussed.