Application of the extended corresponding states (ECS) technique to thermodynamic and transport proper-ties has demonstrated that there are different requirements for conformality of data for these two sets of properties. In addition to the thermodynamic shape factors, derived from accurate equations of state for both the target and reference fluids. there is a need for an additional shape factor, derived from transport property data of the target fluid, in order to fit the transport properties. As a result, a new ECS model is proposed here for viscosity, which uses a single, new viscosity shape factor. which is generated just from viscosity data over the available Pp T surface. In this way, there is no need to determine shape factors from thermodynamics. By application to ethane and refrigerant R134a, it is shown that the scale function is a smooth function of temperature and pressure. The scale function is then represented through a neural network, because of the flexibility and the high data fitting capability of that technique. The accuracy of viscosity data representation on the basis of this model is similar to that obtained with the conventional approach, by summing the dilute gas, the excess and the critical enhancement contributions. The non-theoretical and completely heuristic nature of the model also allows its application to the statistical screening of the experimental data. Furthermore, the variables conversion T. P --> T, rho does not necessarily require an equation of state, as is the case for the historic ECS transport properties model, and this can be carried out by a density model like that recently presented by Cristofoli and co-workers [1,2] for the family of refrigerant fluids.