Summary. Automatic pattern recognition is usually considered as an engineering area studying the development and evaluation of systems that imitate or assist the human ability of recognizing patterns. It may, however, also be considered as a science that studies the natural phenomenon that human beings (and possibly other biological systems) are able to discover, distinguish and characterize patterns in their environment, and identify new observations accordingly. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By that, scientific understanding is achieved of what is needed in order to recognize patterns. Like in any science understanding can be gained from different, sometimes opposite viewpoints. We will introduce the main approaches to the science of pattern recognition as two dichotomies of complementary scenarios, giving rise to four different schools. These schools are roughly defined under the terms of expert systems, neural networks, structural and statistical pattern recognition. We will briefly describe what has been achieved by these schools, what is common and what is specific, which limitations are encountered and which perspectives arise for the future. Finally, we will focus on the challenges facing pattern recognition in the decennia to come. They deal mainly with weaker assumptions to make procedures for learning and recognition wider applicable, others need to develop new formalisms. 1