Having in mind the task of improving the solving methods for Answer Set Programming (ASP), there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers, or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of- the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis.In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, given the features of the instances in a training set and the solvers performance on these instances, we apply a classification method to inductively learn algorithm selection strategies to be applied to a test set. We report the results of an experiment considering solvers and training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve a higher number of instances compared with any solver that entered the 3rd ASP competition.
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