This paper describes the application of neural network techniques to the papermaking industry, particularly for the prediction of paper "curl." Paper curl is an important quality measure that can only be measured reliably off-line after manufacture, making it difficult to control. Here we predict, before paper manufacture from characteristics of the current reel, whether the paper curl will be acceptable and the level of curl. For both the case of predicting the probability that paper will be "out-of-specification" and that of predicting the level of curl, we include confidence intervals indicating to the machine operator whether the predictions should be trusted. The results and the associated discussion describe a successful application of neural networks to a difficult, but important, real-world task taken from the papermaking industry. In addition the techniques described are widely applicable to industry where direct prediction of a quality measure and its acceptability are desirable, with a clear indication of prediction confidence.