In the real world, images always have several visual objects instead of only one, which makes it difficult for traditional object recognition methods to deal with them. In this paper, we propose an ensemble method for multi-label image classification. First, we construct an ensemble of k-labelset classifiers. A voting technique is then employed to make predictions for images based on the created ensemble of k-labelset classifiers. We evaluate our method on Corel dataset and demonstrate the precision, recall and F1 measure superior to the state-of-the-art methods.