Protein expression profiling is defined in general as identifying the proteins expressed in a particular tissue, under a specified set of conditions and at a particular time, usually compared to expression in reference samples. This information is useful in drug discovery and diagnosis as well as in understanding response mechanisms at the protein level. We may identify all the proteins responding to a particular stimulus and select those whose expression changes most. Or we may isolate significant protein variables and then identify them. These definitive sets of proteins (protein expression signatures; PES) are specific to diseases, toxicants, physical stresses, and to degrees of stress severity. Here we describe a method, based on machine learning, for isolating the sets of proteins, before identifying them by name, which classify accurately the treatment classes in a study. The principle in this chapter is that if proteins associated with known classes of interest can be used to identify unknown classes then the proteins are definitive for diagnosis. The proteins in each class, including controls, are converted to digital data and serve as input to artificial neural network (ANN) models.