Metabolomics is a powerful method of examining the intricate connections between mutations, metabolism, and disease. Metabolic footprinting examines the extracellular metabolome or exometabolome. We employed NMR-based metabolic footprinting and multivariate statistical analysis to examine a yeast model of mitochondrial dysfunction. Succinate dehydrogenase (SDH) is a component of both the tricarboxylic acid cycle and the mitochondrial respiratory chain. Mutations in the human SDH are linked to a variety of cancers or neurodegenerative disorders, highlighting the genotype/phenotype complexity associated with SDH dysfunction. To gain insight into the underlying global metabolic consequences of SDH dysfunction, we examined the metabolic footprints of SDH3 and SDH4 mutants. We identified and quantified 36 metabolites in the exometabolome. Our results indicate that SDH mutations cause significant alterations to several areas of yeast metabolism. Multivariate statistical analysis allowed us to discriminate between the different metabotypes of individual mutants, including mutants that were phenotypically indistinguishable. Metabotypes were highly correlated to mutant growth yields, suggesting that the characterization of metabotypes offers a rapid means of investigating the phenotype of a new mutation. Our study provides novel insight into the metabolic effects of SDH dysfunction and highlights the effectiveness of metabolic footprinting for examining complex disorders, such as mitochondrial diseases.