This article provides a comprehensive tutorial on the use of machine learning classifiers for analyzing fMRI data. It begins by introducing the concept of classifiers and their application in neuroimaging, where they predict stimulus categories from fMRI data. The authors outline the process of classifier-based analysis, emphasizing the importance of selecting appropriate features, choosing the right classifier, and evaluating results. They discuss various types of classifiers, including linear and nonlinear models, and provide guidelines for feature selection and dimensionality reduction. The article also covers cross-validation techniques to ensure reliable accuracy estimates and methods for assessing the statistical significance of classification results. Finally, it addresses the evaluation of multiple classifier results and the interpretation of findings. The authors aim to provide a structured approach to using classifiers in fMRI research, highlighting the strengths and weaknesses of different methods and offering practical recommendations.This article provides a comprehensive tutorial on the use of machine learning classifiers for analyzing fMRI data. It begins by introducing the concept of classifiers and their application in neuroimaging, where they predict stimulus categories from fMRI data. The authors outline the process of classifier-based analysis, emphasizing the importance of selecting appropriate features, choosing the right classifier, and evaluating results. They discuss various types of classifiers, including linear and nonlinear models, and provide guidelines for feature selection and dimensionality reduction. The article also covers cross-validation techniques to ensure reliable accuracy estimates and methods for assessing the statistical significance of classification results. Finally, it addresses the evaluation of multiple classifier results and the interpretation of findings. The authors aim to provide a structured approach to using classifiers in fMRI research, highlighting the strengths and weaknesses of different methods and offering practical recommendations.