To meet the growing demand for translation, post-editing of machine translation output (PEMT) is being increasingly adopted as a mainstream alternative working method. The compelling reason behind this trend is the widely reported increase in productivity compared to human translation together with a comparable and sometimes higher quality level. The skills required for post-editing are different from those needed for the editing of author-written texts and different from those required for translation. This workshop aims to familiarize attendees with post-editing methods by analysing the typical mistakes of both neural and statistical machine translation (MT). It also provides some insight into why certain errors occur in raw MT output through a presentation of the historical development of the technology. It will conclude with a discussion of when PEMT should and should not be used and how raw MT output can be improved through preparatory steps.