High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm , (Caenorhabditis elegans), zebrafish (Danio rerio:), fruitfly (Drosophila melanogaster) and mouse (Mus musculus:) phenotypes. Our approach is based on the assumption that, if a mutation in a gene G leads to a phenotypic abnormality in a process P, then G must have been involved in P, either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal denfiitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes cataloged by consortia that are not recorded in the literature.