The repertoire of biochemicals (or small molecules) present in cells, tissue, and body fluids is known as the metabolome. Today, clinicians utilize only a very small part of the information contained in the metabolome, as revealed by the quantification of a limited set of analytes to gain information on human health. Examples include measuring glucose or cholesterol to monitor diabetes and cardiovascular health, respectively. With a focus on comprehensively studying the metabolome, the rapidly growing field of metabolomics captures the metabolic state of organisms at the global or "-omics" level. Given that the overall health status of an individual is captured by his or her metabolic state, which is a reflection of what has been encoded by the genome and modified by environmental factors, metabolomics has the potential to have a great impact upon medical practice by providing a wealth of relevant biochemical data. Metabolomics promises to improve current, single metabolites-based clinical assessments by identifying metabolic signatures (biomarkers) that embody global biochemical changes in disease, predict responses to treatment or medication side effects (pharmachometabolomics). State of the art metabolomic analytical platforms and informatics tools are being used to map potential biomarkers for a multitude of disorders including those of the central nervous system (CNS). Indeed, CNS disorders are linked to disturbances in metabolic pathways related to neurotransmitter systems (dopamine, serotonin, GABA and glutamate); fatty acids such as arachidonic acid-cascade; oxidative stress and mitochondrial function. Metabolomics tools are enabling us to map in greater detail perturbations in many biochemical pathways and links among these pathways this information is key for development of biomarkers that are disease-specific. In this review, we elaborate on some of the concepts and technologies used in metabolomics and its promise for biomarker discovery. We also highlight early findings from metabolomic studies in CNS disorders such as schizophrenia, Major Depressive Disorder (MDD), Bipolar Disorder (BD), Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).