In the Pattern Recognition field, growing interest has been shown in recent years for Multiple Classifier Systems and particularly for Bagging, Boosting and Random Sub- spaces. Those methods aim at inducing an ensemble of classifiers by producing diversity at different levels. Fol- lowing this principle, Breiman has introduced in 2001 an- other family of methods called Random Forest. Our work aims at studying those methods in a strictly pragmatic ap- proach, in order to provide rules on parameter settings for practitioners. For that purpose we have experimented the Forest-RI algorithm, considered as the Random Forest ref- erence method, on the MNIST handwritten digits database. In this paper, we describe Random Forest principles and re- view some methods proposed in the literature. We present next our experimental protocol and results. We finally draw some conclusions on Random Forest global behavior ac- cording to their parameter tuning.