To facilitate root cause analysis in the manufacturing industry, maintenance technicians often fill out “maintenance tickets” to track issues and corresponding corrective actions. A database of these maintenance-logs can provide problem descriptions, causes, and treatments for the facility at large. However, when similar issues occur, different technicians rarely describe the same problem in an identical manner. This leads to description inconsistencies within the database, which makes it difficult to categorize issues or learn from similar cause-effect relationships. If such relationships could be identified, there is the potential to discover more insight into system performance. One way to address this opportunity is via the application of natural language processing (NLP) techniques to tag similar ticket descriptions, allowing for more formalized statistical learning of patterns in the maintenance data as a special type of short-text data. This paper showcases a proof-of-concept pipeline for merging multiple machine learning (ML) and NLP techniques to cluster and tag maintenance data, as part of a broader research thrust to extract insight from largely unstructured natural-language maintenance logs. The accuracy of the proposed method is tested on real data from a small manufacturer.