This chapter deals with pattern recognition and contains theoretical discussions and MATLAB coding examples, including Basic MATLAB functions, Computer Vision System Toolbox functions, and Statistics and Machine Learning Toolbox functions, related to the following topics: saving workspace variables to MAT files, loading variables from MAT files to workspace, working with the Fisher-Iris data set, reading multiple media files from arbitrary folders, using the image datastore, pre-processing steps, feature extraction techniques like minimum eigenvalue method, Harris corner detector, features from accelerated segment test algorithm, maximally stable extremal regions algorithm, speeded up robust features algorithm, KAZE algorithm, binary robust invariant scalable keypoint algorithm, local binary pattern algorithm, histogram of oriented gradients algorithm; clustering techniques like similarity metrics, k-means clustering algorithm, k-medoids clustering algorithm, hierarchical clustering algorithm, Gaussian Mixture Model-based clustering algorithm; classification techniques like k-nearest neighbor classifiers, artificial neural network classifiers, decision tree classifiers, discriminant analysis classifiers, naïve Bayes classifier, support vector machine classifiers, classification learner app; and performance evaluation techniques like silhouette value, Calinski–Harabasz index, and confusion matrix.