Pattern recognition aims to classify observed patterns into predefined categories based on their structural or statistical characteristics. This chapter introduces the fundamental concepts of pattern and image recognition, the composition of a typical recognition system, and the relationship between image processing, recognition, and understanding. Classical recognition approaches—including template matching, statistical methods, syntactic recognition, and neural-network-based models—are described alongside practical examples. The chapter also presents the biomimetic pattern recognition (BPR) framework, which emphasizes similarity and homology-continuity within a class of samples. Finally, a face image recognition case study illustrates feature extraction, preprocessing, and classification procedures, providing a comprehensive overview of pattern recognition techniques in machine vision.