The goal of this lecture is to familiarize the students with the overall pipeline of a Pattern Recognition System. The various steps involved from data capture to pattern classification are presented. The lectures start with a short introduction, where the nomenclature is defined. Analog to digital conversion is briefly discussed with a focus on how it impacts further signal analysis. Commonly used preprocessing methods are then described. A key component of Pattern Recognition is feature extraction. Thus, several techniques for feature computation will be presented including Walsh Transform, Haar Transform, Linear Predictive Coding, Wavelets, Moments, Principal Component Analysis and Linear Discriminant Analysis. The lectures conclude with a basic introduction to classification. The principles of statistical, distribution-free and nonparametric classification approaches will be presented. Within this context we will cover Bayesian and Gaussian classifiers, as well as artificial neural networks. The accompanying exercises will provide further details on the methods and procedures presented in this lecture with particular emphasis on their application.