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This paper suggests a taxonomy for labeling deviating patterns in district heating (DH) customer data. The taxonomy contains several fault labels intended to register information about faults in the DH systems that cause deviations in customer data. This taxonomy is needed because the DH industry is currently missing a unanimous way to label identified faults. The lack of a taxonomy makes it hard to develop automated fault detection and diagnosis methods based on the analysis of DH customer data. Such methods usually require training on historical data sets known to contain deviating data patterns caused by specific faults. By developing a taxonomy for labeling these faults, this study aims to create value for DH utilities in current and future DH systems. The taxonomy structure was based on literature studies, workshops, and discussions with partners within the Swedish FutureHeat collaboration organization Smart Energi. Once the basic structure was decided, it was sent out for evaluation amongst Swedish DH utilities. The evaluation was carried out as a survey study. The results from the survey were compiled, and the finalized version of the deviation cause taxonomy was produced. The study includes the results of the survey study and the finalized version of the deviation cause taxonomy.