This paper focuses on applying of artificial neural networks (ANNs) for the incompatibility detection between an active pharmaceutical ingredient (API) and excipients on the basis of thermogravimetric data. In binary model mixtures, caffeine was used as API mixed with selected excipients. The interpretation of ANN results was based on the assumption that the model mixtures with relatively high content of API (70 and 90%), should undergo similar course of thermal decomposition and be placed in nearby neurons, as opposed to mixtures where excipient predominated. When different behaviour of model mixtures was observed, the incompatibility between mixture ingredients was determined. The results indicate ANNs combined with thermogravimetry to be a simple diagnostic tool that visualizes the behaviour of ingredients in binary mixtures by placing them in different neurons of the topological map so as to determine incompatibility occurrence. The findings were confirmed with complementary techniques - DSC, FTIR and XRPD.