Despite its potential, the applicability of CRNNs has not yet been demonstrated for heterogeneous catalysis and its inherent problems like different time scales for sorption processes and reactions or the presence of many short-lived or hard to measure intermediates. Furthermore, although contained within the CRNN framework, the reversibility of reactions has never been explicitly described and thus no clear distinction between forward and backward reactions can be made in the original formalism. In regards to these challenges, the heterogeneous chemical reaction neural network (hCRNN), an extension to the homogeneous case, is presented to address them. Consequently, the constructed framework will be systematically evaluated for plausible kinetics encountered in heterogeneous catalysis. Preliminary experiments, which employed the new formalism in conjunction with complex kinetics, suggested the existence of an intrinsic link between the kinetic properties of the mechanism under consideration and the training success of the hCRNN, which is to be investigated in this work. Thus, the focus of this work is to unveil the possibilities and limitations of parameter identification of hCRNNs for highly transient generic heterogeneous reaction networks. As will be shown hereafter, aforementioned features of heterogeneous reaction systems strongly effect the interpretability of the obtained results.