In this study we propose to investigate statistical language models for Arabic. Several experiments using different smoothing techniques have been carried out on a small corpus extracted from a daily newspaper. The sparseness of the data leads us to investigate other solutions without increasing the size of the corpus. A word segmentation technique has been employed in order to increase the statistical viability of the corpus. This leads to a better performance in terms of normalized perplexity.