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The task of answer selection in community question answering consists of identifying pertinent answers from a pool of user-generated comments related to a question. The recent SemEval-2015 introduced a shared task on community question answering, providing a corpus and evaluation scheme. In this paper we address the problem of answer selection in Arabic. Our proposed model includes a manifold of features including lexical and semantic similarities, vector representations, and rankings. We investigate the contribution of each set of features in a supervised setting. We show that employing a feature combination by means of a linear support vector machine achieves a better performance than that of the competition winner (F1 of 79.25 compared to 78.55).