This paper addresses the topic of knowledge discovery from data in the context of Arts. In particular, the paper aims to examine the “distant listening” approach to uncover and interpret patterns and rules behind archaeology-inspired music. This approach employs quantitative data mining and graphical visualisation tools to identify and present discovered patterns in music data. For the purpose of the current study, a set of classical music pieces motivated by UNESCO-listed world heritage ‘stecci’ was collected and used as a source of arts data. The collected data were stored in a spreadsheet file and then analysed using Palladio software for network analysis and visualisation. The resulting relational graph enabled easy visual exploration of associations between stecci and musical forms and styles inspired by these archaeological artefacts. However, these findings need to be interpreted with caution due to present limitations. Further research is necessary that would replicate and extend current study to other contexts and questions.