The performance of data based monitoring algorithms is crucially dependent on the ability to discriminate between patterns of normal and fault data. In this paper, we analyze discriminatory properties of PCA, FDA and nonlinear scaled version of PCA algorithm proposed by (Ding et al., 2002). We demonstrate improved discriminatory performance of the nonlinearly scaled PCA over traditional algorithms like PCA and FDA. The scaling and discrimination issues have been analyzed for each of the above algorithms using normal and fault data generated from the bench-marked Tennessee Eastman (TE) problem. The TE problem is used to highlight the superiority of the nonlinear scaled PCA (SPCA) over PCA and FDA. © Copyright 2007 IFAC.