The English translation of the Swedish compound fönsterbräda into windowsill, or the proper noun Danmark into Denmark makes perfect sense. But how about the compound fossilbränslefri as simply fossil fuel or the name Mälaren as Lake?  All four of these translations have been produced with the help of automatic machine translation. The aim of this paper is to present the expanding field of application of machine translation and some issues related to this type of translation. More specifically, the study has looked at Google Translate as one of the most commonly used machine translation systems online, and how it responds to the two linguistic categories that were selected for this small study: compound nouns and proper nouns. Besides analyzing these categories, two different text types were chosen: general information articles from a local authority website (Stockholm City) and patent texts, both of which belong to the expanding field of application of Google Translate. The results of the study show that in terms of compound nouns, neither of the text types proved to be significantly better suited for machine translation than the other and neither had an error rate below 20 %. Most of the errors related to words being erroneously omitted in the English output and words which were incorrectly translated in relation to context. As for proper nouns, the patent texts contained none and subsequently no error analysis could be made, whereas the general information articles included 76 proper nouns (out of a total word count of 810). The most prominent error related to the Swedish version not being maintained in the English output where it should have been, e.g. translating Abrahamsberg as Abraham rock. The errors in both of the linguistic categories had varying impact on the meaning of the texts, some of which distorted the meaning of the word completely, and some which were of minor importance. This factor, along with the fact that the reader of the text influences how the comprehension level of the text is perceived through their language and subject knowledge, makes it difficult to evaluate the full impact of the various errors. It can, however, be said that patent text could pose as a better option for machine translation than general information articles in relation to proper nouns, as this text type is likely to contain no or very few proper nouns.