In this paper, we show an active object recognition system. This system uses a mutual information framework in order to choose the optimal parameters of an active camera for recognizing an unknown object. In a learning step, our system builds a database of all objects by means of a controlled acquisition process over a set of actions. These actions are taken from the set of different feasible configurations for our active sensor. Actions include pan, tilt and zoom values for an active camera. For every action, we compute the conditional probability density of observing some features of interest in the objects to recognize. Using a sequential decision making process, our system determines an optimal action that increase discrimination between objects in our database. This procedure iterate until a decision about the class of the unknown object can be done. We use the color patch mean over an interest region in our image as the discrimination feature. We have used a set 8 different soda bottles as our test objects and we have obtained a recognition rate of about 99%. The system needs to iterate about 4 times (that is, to perform 4 actions) before being capable of making a decision.