Feature selection is a key issue in many machine learning applications and the need to test lots of candidate features is real while computational time required to do so is often huge. In this paper, we introduce a parallel version of the well- known AdaBoost algorithm to speed up and size up feature selection for binary classification tasks using large training datasets and a wide range of elementary features. This parallelization is done without any modification to the AdaBoost algorithm and designed for PC clusters using Java and the JavaSpace distributed framework. JavaSpace is a memory sharing paradigm implemented on top of a virtual shared memory, that appears both efficient and easy-to-use. Results and performances on a face detection system trained with the proposed parallel AdaBoost are presented.