We present a new method based on artificial neural networks trained with multiseed backpropagation, for displaying an n-dimensional distribution in a projected space of one, two, or three dimensions. As principal component analysis (PCA) the proposed method is useful for extracting information on the structure of the data set, but unlike the PCA the transformation between the original distribution and the projected one is not restricted to be linear. Artificial examples and real astronomical applications are presented in order to show the reliability and potential of the method for the analysis of large astronomical data sets.