Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifying autoencoder (CAE). This machine learning technique uses specialized architecture of artificial neural networks to provide insight into typically opaque classification processes. The method proves to be robust to noisy and limited data sets, as well as being capable of disentangling relatively complicated rules over data sets. We demonstrate its capabilities by applying the technique to an experimental database (the Waltz database) and demonstrate the CAE's capability to provide insight into a novel descriptor, dimeric isotropic deviation-an experimental measure of the aggregation properties of the amino acids. We measure this value for all 20 of the common amino acids and find correlation between dimeric isotropic deviation and the failure to form amyloids when hydrophobic effects are not a primary driving force in amyloid formation. These applications show the value of the new method and provide a flexible and general framework to approach problems in biochemistry using artificial neural networks.