This paper presents a thermo-economic optimization of a combined cycle power plant obtained via the Proper Orthogonal Decomposition-Radial Basis Functions (POD-RBF) procedure. POD, also known as "Karhunen-Loewe decomposition" or as "Method of Snapshots" is a powerful mathematical method for the low-order approximation of highly dimensional processes for which a set of initial data is known in the form of a discrete and finite set of experimental (or simulated) data: the procedure consists in constructing an approximated representation of a matricial operator that optimally "represents" the original data set on the basis of the eigenvalues and eigenvectors of the properly re-assembled data set. By combining POD and RBF it is possible to construct, by interpolation, a functional (parametric) approximation of such a representation. In this paper the set of starting data for the POD-RBF procedure has been obtained by the CAMEL-Pro (TM) process simulator. The proposed procedure does not require the generation of a complete simulated set of results at each iteration step of the optimization, because POD constructs a very accurate approximation to the function described by a relatively small number of initial simulations, and thus "new" points in design space can be extrapolated without recurring to additional and expensive process simulations. Thus, the often taxing computational effort needed to iteratively generate numerical process simulations of incrementally different configurations is substantially reduced by replacing much of it by easy-to-perform matrix operations. The object of the study was a fossil-fuelled, combined cycle powerplant of nameplate power P-e1 = 160 - MW, for which a set of operational and costing data was available: different combinations of the relevant process parameters were considered and the corresponding process variables calculated for each simulation were collected. In our case the aim of the application of the procedure was to find the combination of process parameters which corresponds to the minimum the thermo-economic cost of the products. The results obtained by the application of this model have been validated by comparison with literature data obtained by the application of genetic algorithm optimization. (C) 2014 Elsevier Ltd. All rights reserved.