Projection pursuit is a method for finding interesting projections of high-dimensional multivariate data. Typically interesting projections are found by numerical maximizing some measure of non-normality of projected data (so-called projection index) over projection direction. The problem is to select the index for projection pursuit. In this article we compare performance of five projection indices: projection indices based on omega2, Omega2, Kolmogorov-Smirnov goodness-of-fit measures, entropy index and Friedman's index. It is supposed that observed random variable satisfies a multidimensional Gaussian mixture model.