State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework, where the knowledge of a human translator is combined with the MT system. We present a statistical IMT system able to learn from user feedback by means of the application of online learning techniques. These techniques allow the MT system to update the parameters of the underlying models in real time.