Proteins extracted, separated, and visualized can provide detailed information about an organism and its environment. We have used an artificial neural network model to identify significant exposures of a cladoceran (Daphnia magnet) to alcohol and pesticides, of a copepod (Eurytemora affinis) to heat and salinity, of an earthworm (Lumbricus terrestris) to sulfur mustard and of a small fish (Oryzias latipes) to groundwater concentrations. The method depends on systematic differences or tendencies in numbers and amounts of proteins present in different treatments or environments. We illustrate how neural computing might be useful in retrieving the information contained in the hundreds or thousands of proteins expressed in test organisms. Such information could apply to prediction of toxicity, identification of toxicity and to characterizing environments in general.