Technical Abstract: Plant breeders, geneticists and agronomists are increasingly faced with theoretical and practical questions of multivariate nature. With increases in germplasm sizes, number of plant and crop variables and evaluation and characterization data on molecular, biochemical, morphological and agronomic traits, multivariate statistical analysis (MVAs) methods are receiving increasing interest and assuming considerable significance. Some MVAs, such as multivariate analysis of variance (MANOVA) and co-variance (MANCOVA), are extensions of uni- and bivariate statistical methods appropriate for significance tests of statistical hypotheses; however, most MVAs are used for data exploration, extraction of fundamental components of large data sets, discovery of latent structural relationships, visualization and description of biological patterns. This review focuses on the salient features and applications of MVAs in multivariate data analyses of plant breeding, genetics and agronomy data. These include MANOVA and MANCOVA, general and mixed regression models for hierarchical and multivariate data analysis, data reduction methods (factor, principal components, perceptual mapping and correspondence analyses), data classification methods (discriminant analysis, clustering and additive trees, conjoint analysis and multidimensional scaling) and categorical data analysis.