Contrary to perceived wisdom, we explore the role of machine translation (MT) in assisting with the translation of literary texts, considering both its limitations and its potential. Our motivations to explore this subject are twofold, arising from: (1) recent research advances in MT, and (2) the recent emergence of the ebook, which together allow us for the first time to build literature-specific MT systems by training statistical MT models on novels and their professional translations. A key challenge in literary translation is that one needs to preserve not only the meaning (as in other domains such as technical translation) but also the reading experience, so a literary translator needs to carefully select from the possible translation options. We explore the role of translation options in literary translation, especially in the context of the relatedness of the languages involved. We take Camus’ L’Étranger in the original French language and provide qualitative and quantitative analyses for its translations into English (a less-related language) and Italian (more closely related). Unsurprisingly, the MT output for Italian seems more straightforward to be post-edited. We also show that the performance of MT has improved over the last two years for this particular book, and that the applicability of MT does not only depend on the text to be translated but also on the type of translation that we are trying to produce. We then translate a novel from Spanish-to-Catalan with a literature-specific MT system. We assess the potential of this approach by discussing the translation quality of several representative passages.