In this article we provide a linguistic analysis of a corpus of machine-translated texts from English into French, in comparison with a corpus of original French. Two machine translation tools have been selected: one of them is a generic, general public, neural system while the other is a specific, statistical-based tool developed by a major international organization. Following the corpus-based translation studies approach, we provide a quantitative analysis of a series of linguistic features (lexical and grammatical) which are known to be problematic for English-French translators. We aim to show that linguistic usage, which goes beyond grammatical correctness and needs to be taken into account to provide natural, idiomatic translations in order to meet the demands of today’s translation market, is not taken into account by current machine translation systems. Our aim is to show human translators’ added value over such systems, in particular for translation trainees.