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This paper presents an experimental comparison of different features for the detection of replay spoofing attacks in Automatic Speaker Verification systems. We evaluate the proposed countermeasures using two recently introduced databases, including the dataset provided for the ASVspoof 2017 challenge. This challenge provides researchers with a common framework for the evaluation of replay attack detection systems, with a particular focus on the generalization to new, unknown conditions (for instance, replay devices different from those used during system training). Our cross-database experiments show that, although achieving this level of generalization is indeed a challenging task, it is possible to train classifiers that exhibit stable and consistent results across different experiments. The proposed approach for the ASVspoof 2017 challenge consists in the score-level fusion of several base classifiers using logistic regression. These base classifiers are 2-class Gaussian Mixture Models (GMMs) representing genuine and spoofed speech respectively. Our best system achieves an Equal Error Rate of 10.52% on the challenge evaluation set. As a result of this set of experiments, we provide some general conclusions regarding feature extraction for replay attack detection and identify which features show the most promising results.