An approach to improve accuracy of incorrect domain theories is presented that learns concept descriptions from positive and negative examples of the concept. The method uses the available domain theory, that might be both overly general and overly specific, to group training examples before attempting concept induction. GENTRE is a system that has been implemented to test the performance of the method. GENTRE is not limited to variable-free, function-free or non-recursive domains as many other approaches. In the paper we present results from experiments in three different domains and compare the performance of GENTRE with that of ID3 and IOU. The learned concept descriptions are consistent with training examples and have an improved classification accuracy relative to the original domain theory.