Parity equation based residual generation with a statistical decision-making scheme has been proved to be effective for fault detection and isolation (FDI) in process systems. However, to implement a statistical decision-making scheme on residuals generated through parity equations, a multivariate probability distribution function of the residuals must be identified for each fault class. Often, the statistical properties of these residuals depend on the noise characteristics associated with the measurement systems, in addition to modeling errors. However, identification of the correct probability distribution function for the noise is a nontrivial task. The issue is further complicated due to the effects of the noise propagating through the transformation designed for minimizing the effects of the normal operating envelope and disturbances. To overcome such problems, a support vector machine (SVM) classification algorithm can be used in lieu of a statistical decision-making scheme. Optimal fault classifications can be achieved under the SVM framework using properly designed hyper-planes. To process the residuals using SVMs, several additional issues have to be dealt with, such as the non-linear classification problem, over-fitting issues, and multi-class fault classification. The objective of this paper is to describe a framework within which SVMs can be effectively integrated with parity equation based residual generation schemes. The developed framework has been evaluated on a physical process control platform under various fault scenarios. It has been shown that the scheme is feasible and particularly suitable for situations where it is difficult to obtain probability distribution functions for all potential fault classes. (C) 2019 Elsevier Ltd. All rights reserved.