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This paper investigates booststrapping part-of-speech taggers using co-training, in which two taggers are iteratively re-trained on each other's output. Since the output of the taggers is noisy, there is a question of which newly labelled examples to add to the training set. We investigate selecting examples by directly maximising tagger agreement on unlabelled data, a method which has been theoretically and empirically motivated in the co-training literature. Our results show that agreement-based co-training can significantly improve tagging performance for small seed datasets. Further results show that this form of co-training considerably outperforms self-training. However, we find that simply re-training on all the newly labelled data can, in some cases, yield comparable results to agreement-based co-training, with only a fraction of the computational cost.