Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to Nuclear Magnetic Resonance data and the introduction of Statistical Total Correlation Spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated 'systems medicine' approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.