Response surfaces or metamodels have long been used as cost effective surrogates for complex numerical simulations or as analytical representations of experimental data. In standard practice, a single analytical model is used within a simulation or fit to experimental data. In reality, several different models may be appropriate for consideration. A new method is presented for combining a set of alternative metamodels into a single master response surface or Super-Metamodel (SM) that is valid over all of input parameter space. The SM is formed as a linear combination of competing component metamodels where each model is weighted by its predicted probability.