We model a part of a process in pulp to paper production using feed forward connected neural networks. A set of parameters related to paper quality is predicted from a set of process values. The predicted values are results from laboratory experiments which are time consuming. We check for irrelevant inputs and we manage with training sets that are considered small. The output vector is separated into single values which are predicted on different architectures adapted to each output. A strategy that continuously adapts the process model seems to be useful. In this work the backprop learning algorithm been used. possessed about one state of one variable with one state of another variable, and is used for calculation of a posterior probability distribution conditioned on a set of given input events. It is used as a measure of disproportionality in data mining