In this paper, we present an approach to developing resources for a low-resource language, taking advantage of the fact that it is closely related to languages with more resources. In particular, we test our approach on Macedonian, which lacks tools for natural language processing as well as data in order to build such tools. We improve the Macedonian training set for supervised part-ofspeech tagging by transferring available manual annotations from a number of similar languages. Our approach is based on multilingual parallel corpora, automatic word alignment, and a set of rules (majority vote). The performance of a tagger trained on the improved data set of 88% accuracy is significantly better than the baseline of 76%. It can serve as a stepping stone for further improvement of resources for Macedonian. The proposed approach is entirely automatic and it can be easily adapted to other language in similar circumstances.