The paper introduces an R package called MVN, which is designed to assess multivariate normality. Multivariate normality is crucial for the validity of many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, and canonical correlation. The package includes three widely used multivariate normality tests: Mardia’s, Henze-Zirkler’s, and Royston’s, along with graphical methods like chi-square Q-Q plots, perspective and contour plots. It also offers functions to check univariate normality and detect multivariate outliers using robust Mahalanobis distances. The authors provide two illustrative examples to demonstrate the package's functionality and introduce a user-friendly web application for non-R users. The MVN package aims to provide a comprehensive and flexible tool for assessing multivariate normality, combining both statistical tests and graphical approaches to make more reliable decisions.The paper introduces an R package called MVN, which is designed to assess multivariate normality. Multivariate normality is crucial for the validity of many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, and canonical correlation. The package includes three widely used multivariate normality tests: Mardia’s, Henze-Zirkler’s, and Royston’s, along with graphical methods like chi-square Q-Q plots, perspective and contour plots. It also offers functions to check univariate normality and detect multivariate outliers using robust Mahalanobis distances. The authors provide two illustrative examples to demonstrate the package's functionality and introduce a user-friendly web application for non-R users. The MVN package aims to provide a comprehensive and flexible tool for assessing multivariate normality, combining both statistical tests and graphical approaches to make more reliable decisions.