Abstract: The present invention provides a rule based process control system which automatically learns how to control a process by inducing control rules from the process as it changes. During process operation data samples from the process are collected which include the controllable variables in the process along with the dependent variable (goal or goals) of the process. If a sample is not predicted by the current rules that control the process, new rules are induced from the new sample and previously collected samples by creating a decision tree. The controllable variable having the greatest effect on the goal occupies the highest tree node. New rules are produced from the decision tree which predict process behavior based on the new and old samples. From these new rules, control rules which satisfy system constraints and improve on the goal(s) are selected. The control rules are then used to control the process until another sample is not predicted by the new rules.