Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific 'Metabolite Atlases'.