1. A segmentation and classification process, comprising the steps of: determining if a data base includes classes of representative sample units to be discriminated for which a statistical law exists defining said classes of representative sample units; estimating statistical laws defining said classes of representative sample units, when said determining step determines that said statistical law is not available; ordering said classes of representative sample units using characteristic variables of said classes; segmenting said database in at least as many parts as appear in said database by determining splits discriminating the ordered classes by comparing the ordered classes with each other; and classifying an input sample by performing the steps of: determining a probability of said input sample being included in each of said classes by evaluating at least one characteristic variable of said input sample; classifying said sample into one of said classes having a highest probability of including said sample; wherein said method is a shape recognition and discrimination method and the sample units contain physical features used for shape recognition and discrimination and said at least one characteristic variable which is evaluated includes at least one of color, size, surface, and volume.