The design of a pattern recognition system essentially involves the following three aspects such as data acquisition and preprocessing, data representation, and decision making. The problem domain dictates the choice of sensor(s), preprocessing technique, representation scheme, and the decision-making model. It is generally agreed that a well-defined and sufficiently constrained recognition problem (small intra-class variations and large interclass variations) will lead to a compact pattern representation and a simple decision-making strategy. Learning from a set of examples (training set) is an important and desired attribute of most pattern recognition systems. The four best known approaches for pattern recognition are template matching, statistical classification, syntactic or structural matching, and neural networks. These models are not necessarily independent and sometimes the same pattern recognition method exists with different interpretations. Attempts have been made to design hybrid systems involving multiple models.