A significant number of published databases and research papers exist in foreign languages and remain untranslated to date. Important sources of primary scientific information in German are Beilstein Handbuch der Organischen Chemie, Gmelin Handbuch der Anorganischen Chemie, Landolt-Börnstein Zahlenwerte und Funktionen, Houben-Weyl Methoden der Organischen Chemie, fundamental research papers, and patents. Although Reaxys has acquired Beilstein and Gmelin, many original references are still in German since 1770s, and the information presented in printed and online versions is often not duplicated. To read these resources, either costly professional translation services are needed or a reading knowledge of German has to be acquired. A convenient approach is to utilize machine translation for reading German texts; however, there is a question of translation reliability. In this work, several different platforms that employ neural network for machine translation (NMT) were tested for translation capability of scientific German. From a preliminary survey, Google Translate and DeepL were finalized for further studies (German to English). Excerpts from German documents spanning more than a century have been carefully chosen from standard works. DeepL Translator and Google Translate were found to be reliable for converting German scientific literature into English for a wide variety of technical passages. As a benchmark, human and machine translations are compared for complex sentences from old literature and a recent publication. Care and intuition should be used before relying on machine translation of methods and directions in general. Reagent addition (to or from) may be inverted in some synthetic procedures using machine translations.