The concept of “Gaussianization” implies a transformation aimed at changing the distribution of the input random variable to Gaussian. It has been used until now as a means to achieve independence among components in multivariate distributions that in turn was used as a tool for various purposes ranging from density estimation to normalization. In this contribution we propose Gaussianization for pattern recognition applications in support of Gaussianity assumptions made by various classifiers. Previous approaches completely ignore separability considerations, the Gaussianization being conducted over the entire data, irrespective of class affiliation and are not useful for recognition purposes. We instead propose a transform such that the output random variable is distributed according to a Gaussian mixture, where each class accounts for one mixture component. We successfully test our method on both synthetic and real data.