A support vector machine with knowledge incorporation is applied to detect the faults in Tennessee Eastman Process, a benchmark problem in chemical engineering. The knowledge incorporated algorithm takes advantage of the information on horizontal translation invariance in tangent direction of the instances in dataset. This essentially changes the representation of the input data while training the algorithm. These local translations do not alter the class membership of the instances in the dataset. The results on binary as well as multiple fault detection justify the use of knowledge incorporation. (c) 2005 Elsevier Ltd. All rights reserved.