Abstract: Aim of this paper is to show a powerful tool for data mining activities based on a nonlinear latent variable model, i.e. Probabilistic Principal Surfaces (PPS). PPS builds a probability density function of a given data set of patterns, lying in a D-dimensional space, which can be expressed in terms of a limited number of latent variables lying in a Q-dimensional space. Usually, Q is 2 or 3 dimensional and thus the density function is used to visualize the data in the latent space. PPS have been fruitful exploited for classification as well as visualization and clustering of complex real high-D data and represents a promising data mining tool for researchers in genetics and bioinformatics.