Traditionally automatic pattern recognition is based on learning from examples of objects represented by features. In some applications it is hard to define a small, relevant set of features. At the cost of large learning sets and complicated learning systems discriminant functions have to be found. In this paper we discuss the possibility to construct classifiers entirely based on distances or similarities, without a relation with the feature space. This is illustrated by a number of experiments based on the support object classifier (), a derivative of Vapnik's support vector classifier ().