This master thesis aims to investigate a phrase-based approach of Memory-based Machine Translation. This is a form of automatic translation powered by lazy-learning classifiers to translate fragments of the input sentence. A parallel corpus serves as the basis for training such a classifier. In the phrase-based approach the principal component of these fragments is a phrase of arbitrary length. This can be contrasted to prior research in the field in which this component was a single word. A key element in the research is a comparison of three methods of phrase extraction. A new decoder has been developed to deal with the characteristics unique to this approach, and re-assemble the translated fragments into one final translation. This research will show that one of the proposed phrase-extraction methods is capable of outperforming previous word-based approaches, even though this gain is limited and the impact