Technological advancements and widespread adaptation of new technology in industry have made industrial time series data more available than ever before. This trend is expected to continue, especially with the introduction of Industry 4.0, where the goal is to connect everything on the industry floor to the cloud and the Industrial Internet of Things. With this development grows the need for versatile methods for mining industrial time series data. Time series motif discovery is a sub-set of data mining and is about finding interesting patterns in time series data. The state of the art in time series motif discovery is the Matrix Profile proposed in 2016. However, there are few publications where the Matrix Profile has been applied to real-life industrial time series data despite its popularity. The goal of the thesis has been to create a tool that enables joint human-machine exploration of industrial time series data using the Matrix profile and present the challenges involved. The result is a human-machine exploration procedure called IUSE that has been applied to three data sets containing real-life industrial time series data. IUSE enables the user to extract semantic information, detect cycles, find deviating patterns and helps the user to get a deeper understanding of a time series. The description of IUSE comes alongside learned lessons, faced challenges and experience from applying the Matrix Profile to actual industrial time series data.