We consider sublinear test-time algorithms for image categorization when the number of classes is very large. Our method builds upon the label tree approach proposed in , which decomposes the label set into a tree structure and classify a test example by traversing the tree. Even though this method achieves logarithmic run-time, its performance is limited by the fact that any errors made in an internal node of the tree cannot be recovered. In this paper, we propose label forests - ensembles of label trees. Each tree in a label forest will decompose the label set in a slightly different way. The final classification decision is made by aggregating information across all trees in the label forest. The test running time of label forest is still logarithmic in the number of categories. But using an ensemble of label trees achieves much better performance in terms of accuracies. We demonstrate our approach on an image classification task that involves 1000 categories.