In this paper, we examine how methods for evaluating corpora in terms of technical terms can be used for characterising technical documents used as textual materials in translation training in a translation education setup. Technical documents are one of the standard types of textual materials used in translation training courses, and choosing suitable materials for learners is an important issue. In technical documents, technical terms play an essential role. Assessing how terms are used in these documents, therefore, would help translation teachers to choose relevant documents as training materials. As corpus-characterisation methods, we used self-referring measurement of the occurrence of terminology and measurement of the characteristic semantic scale of terms. To examine the practical applicability of these methods to assessing technical documents, we prepared a total of 12 short English texts from the six domains of law, medicine, politics, physics, technology and philosophy (two texts were chosen from each domain), whose lengths ranged from 300 to 1,150 words. We manually extracted terms from each text, and using those terms, we evaluated the nature and status of the textual materials. The analysis shows that even for short texts, the corpus-characterisation methods we provide useful insights into assessing textual materials.