Proper identification of insects in grain storage facilities is critical for predicting development of pest populations and for making management decisions. However, many stored-grain insect pests are difficult to identify, even for trained personnel. We examined the possibility that near-infrared (NIR) spectroscopy could be used for taxonomic purposes based on the premise that every species may have a unique chemical composition. Tests were conducted with 11 species of beetles commonly associated with stored grain. Spectra from individual insects were collected by using a near-infrared diode-array spectrometer. Calibrations were developed by using partial least squares analysis and neural networks. The neural networks calibration correctly identified >99% of test insects as primary or secondary pests and correctly identified >95% of test insects to genus. Evidence indicates that absorption characteristics of cuticular lipids may contribute to the classification of these species. We believe that this technology could be used for rapid, automated identification of many other organisms.