Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.