Modeling the underlying stochastic process is one of the main goals in the study of many dynamic phenomena, such as signal processing, system identication and time series. The issue is often addressed within the framework of ARMA paradigm, so that the related task of the identication of the "true" order is crucial. As it is well known, the eectiveness of such an approach may be seriously compromised by misspecication errors since they may aect model capabilities in capturing dynamic structures

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... f the process. As a result, inference and empirical outcomes may be heavily misleading. Despite the big number of available approaches aimed at determining the order of an ARMA model, the issue is still open. In this paper we bring the problem in the framework of bootstrap theory in conjunction with the information based criterion of Akaike (AIC), and a new method for ARMA model selection will be presented. A theoretical justication for the proposed approach as well as an evaluation of its small sample performances, via simulation study, are given.