This paper introduces the Banditron, a variant of the Perceptron, for the multiclass bandit setting. The multiclass bandit setting models a wide range of practical supervised learning applications where the learner only receives partial feedback (referred to as "bandit" feedback, in the spirit of multi-armed bandit models) with respect to the true label (e.g. in many web applications users often only provide positive "click" feedback which does not necessarily fully disclose a true label). The Banditron has the ability to learn in a multiclass classification setting with the "bandit" feedback which only reveals whether or not the prediction made by the algorithm was correct or not (but does not necessarily reveal the true label). We provide (relative) mistake bounds which show how the Banditron enjoys favorable performance, and our experiments demonstrate the practicality of the algorithm. Furthermore, this paper pays close attention to the important special case when the data is linearly separable --- a problem which has been exhaustively studied in the full information setting yet is novel in the bandit setting.