The main problem when trying to optimize the parameters of libraries, such as MPI, is that there are many parameters that users can configure. Moreover, predicting the behavior of the library for each configuration is non-trivial. This makes it very difficult to select good values for these parameters. This paper proposes a model for autotuning MPI applications. The model is developed to analyze different parameter configurations and is expected to aid users to find the best performance for executing their applications. As part of the AutoTune project, our work is ultimately aiming at extending Periscope to apply automatic tuning to parallel applications. In particular, our objective is to provide a straightforward way of tuning MPI parallel codes. The output of the framework are tuning recommendations that can be integrated into the production version of the code. Experimental tests demonstrate that this methodology could lead to significant performance improvements.