This review is based on both classical and newest original research publications found in prominent journals on plant genetic resources. It exemplifies, illustrates and summarizes statistical methodology for analyzing multivariate data from trials and surveys applied to evaluate phenotypic diversity in germplasm collections. Specific methodological knowledge necessary to select appropriate techniques, carry out data analysis and make proper interpretations of analytical results is discussed. Those uni- and multivariate statistical methods proved effective for evaluating phenotypic diversity in plant genetic resources for quantitative traits are shortly characterized and discussed with respect to their use with the SAS package. Univariate methods are based on random ANOVA model for data originating from a trial carried out in a randomized complete block design and also in a multi-environment trials carried out in the same block design. Pattern analysis is mainly recommended, among multivariate techniques, as an effective procedure to evaluate quantitative phenotypic diversity in the germplasm collections. It is a combined approach including cluster analysis as a classification procedure and principal component analysis or canonical variant analysis as the ordination procedures to show graphically similarities and dissimilarities among accessions and their homogenous groups.
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