Purpose: this paper aims to present a simple method to synthesize an empirically-based model that permit to estimate the maximum displacement of a plate when a shotpeening process values are known. Design/methodology/approach: This approach regards the difficulty to develop a mathematical model to describe the relationship between the shot peening process variables (shot diameter, impact velocity, static preload and coverage) and the curvature of the piece. Such a model was generated through the application of statistical inference methods - multivariable regression and neural networks – to a set of experimental data concerning the application of peen forming processes to a group of 215 aluminium 7050 alloy rectangular plates. Findings: Although the estimated displacements from both models comply reasonably well with the experimental data, the obtained results exposed the superiority of the regressive model concerning accuracy. Research limitations/implications: Shot peen forming, a die less forming process, is one of the most successful methods to produce slight and smooth curvatures on large panels and plates. Through the application of a regulated blast of small round steel shot on the piece surface, a thin internal layer of residual compressive stress causes the elastic stretching of the shotted surface, giving rise to a permanent non-plastic deformation of the whole piece. Although this forming process has been used since the fifties, especially by the aerospatial industry, a scientific method for peen forming process planning has not been developed yet. Originality/value: The referred model can be used as an engineering tool to aid setting up a peen forming process in order to produce a desired curvature on a given plate.