A practical approach to assess the trade-offs in selecting the parameters that der ne the class of candidate models and that are commonly used in the Robust Identification framework is derived. The procedure minimizes the worst case identification error bound and guarantees consistency, according to all the experimental evidence. A consistency curve is defined, and upper and lower bounds are computed to graphically select these parameters.