AbstractThis article presents an approach to fault diagnosis of chemical processes at steadystate operation by using artificial neural networks. The conventional back‐propagation network is enhanced by adding a number of functional units to the input layer. This technique considerably extends a network's capability for representing complex nonlinear relations and makes it possible to simultaneously diagnose multiple faults and their corresponding levels in a chemical process. A simulation study of a heptane‐to‐toluene process at steady‐state operation shows successful results for the proposed a