Controlling pipe extruder industrial processes through supervised, feed forward neural networks. Multi-layer, feed forward perceptron neural networks produce hetero- and auto- pattern associators which can be applied to a wide range of problems. The area of process monitoring and control is one of numerous inputs and outputs, which are normally non-determinative and do not adhere to known probability distributions. By training neural networks through supervised learning, such as back propagation, a mechanized tool can be created which offers advantages over traditional methods based on statistics. Significant benefits are the ability to discern complex relationships and trends rather than assuming distributions (usually Gaussian) or specifying algorithms, the ability to integrate in real time large amounts of continuous data and adapt incrementally to changes in process, and the ability to handle noisy or incomplete data.