The question of effectiveness of the infrared thermal imaging for population screening and the early breast cancer detection has become topical again in last few years. The reason is that we have new ways how to replace the subjective classification, performed by a trained physician and based on ill-defined thermo-pathological features, by semi-automated classification performed on digital thermograms with a sophisticated computer program. Our purpose is to solve the task of a pattern classifier design that would work as a core of such a program, and also try to answer the question of the effectiveness. We describe the regions of interest (whole breasts in frontal picture) by number of about 40 features quantifying all fundamental properties of ROI - from an average temperature up to texture descriptors. Feature selection procedures helped us to define the essential quantitative thermo-pathological features. We used the final feature space to design several types of classifiers with supervised learning.