The aim of classification is to infer type of an unknown sample summarized by its features. Classifier is the machine learning system performing this operation. The classifier should have been trained beforehand for this purpose by labeled data. Hence, we assume that a supervisor has labeled the data to be used in training. To explain the classifier concept, we will start with its definition. Then, we will introduce the Bayes,Bayes classifier k-nearest neighbor (kNN),KNN classifier support vector machine (SVM),SVM classifier and decision tree classifiers.Decision tree classifier While handling each classifier, we will start with its theoretical background. Then, we will explore its training on PC in Python language. Afterward, we will show methods of deploying the trained classifier to the STM32 microcontroller. As end of the chapter applications, we will form classifiers for the accelerometerAccelerometer data, audio signals,Audio signal and digital images.Digital image We will benefit from the extracted features in Chap. 5 for this purpose.