The article presents statistical methods for analyzing body ratios in multivariate morphometry, focusing on the interpretation of results from linear discriminant analysis (LDA) and principal component analysis (PCA) in terms of body proportions. The authors develop two new tools: the "LDA ratio extractor" and the "PCA ratio spectrum." The LDA ratio extractor identifies the most informative body ratios for distinguishing between groups, while the PCA ratio spectrum interprets principal components in terms of body ratios. Additionally, the "allometry ratio spectrum" is introduced to assess the allometric behavior of ratios. The methods are applied to data sets from parasitic wasps and rock crabs, demonstrating their utility in taxonomic studies. The paper also discusses various concepts of size and their relation to multivariate allometry, emphasizing the importance of distinguishing between size and shape in morphometric analysis. The methods are implemented using R statistical software and are designed to be applicable in a statistically consistent framework. The results show that body ratios can provide valuable insights into species differentiation and variation, and that the proposed methods allow for a more intuitive interpretation of multivariate analyses in terms of body proportions. The study highlights the importance of integrating ratio analysis with multivariate statistical methods to enhance the understanding of morphological variation in biological taxa.The article presents statistical methods for analyzing body ratios in multivariate morphometry, focusing on the interpretation of results from linear discriminant analysis (LDA) and principal component analysis (PCA) in terms of body proportions. The authors develop two new tools: the "LDA ratio extractor" and the "PCA ratio spectrum." The LDA ratio extractor identifies the most informative body ratios for distinguishing between groups, while the PCA ratio spectrum interprets principal components in terms of body ratios. Additionally, the "allometry ratio spectrum" is introduced to assess the allometric behavior of ratios. The methods are applied to data sets from parasitic wasps and rock crabs, demonstrating their utility in taxonomic studies. The paper also discusses various concepts of size and their relation to multivariate allometry, emphasizing the importance of distinguishing between size and shape in morphometric analysis. The methods are implemented using R statistical software and are designed to be applicable in a statistically consistent framework. The results show that body ratios can provide valuable insights into species differentiation and variation, and that the proposed methods allow for a more intuitive interpretation of multivariate analyses in terms of body proportions. The study highlights the importance of integrating ratio analysis with multivariate statistical methods to enhance the understanding of morphological variation in biological taxa.