Domain adaptation is a difficult problem, but also a relevant one. Unsupervised domain adaptation focuses on adapting a model from a source domain, which includes plenty of labeled data, to a target domain that provides no labeled data. This is the most compelling setting of domain adaptation, but it is also the most difficult one. We perform an experimental analysis to highlight how difficult this problem is. We show that the best available unsupervised domain adaptation system for POS tagging can be outperformed by a simple POS tagger that has access to only 250 labeled sentences from the target domain. This is not a fair comparison between these two systems, of course; but it highlights that unsupervised domain adaptation is not well solved yet. Moreover, the best available systems are not yet practical, since they are complex, difficult to implement, and do not achieve significant improvements.