In this research, we are attempting to extract relevant knowledge from sensor data of 3D printers. This is in continuation of our previous work to characterize the operational status of 3D printers using Bootstrap-CURE technique, including the post-processing techniques oriented to understand and conceptualize the clusters. Four clusters were identified regarding normal successful printing and abnormal situations due to different reasons like insufficient operating conditions, sensor failure, imbalance in internal temperature, etc. In the current work, the representation of data moves from punctual sensor readings to a vectorial description of a printing job in terms of the sequence of operational status over time. By representing a job by its sequence of clusters, it can be analyzed which sequential patterns are associated with successful or failing jobs. This opens the door to identifying trends and anticipating failures that are crucial to improving 3D printers’ control and management. To cluster qualitative time series, specific qualitative distances are required, like χ2-distance. One of the main challenges in clustering a time series dataset is to similarities among various time series rows. However, when the irregular length of the series and highly skewed series appear, special representation methods are required to avoid biases in results and the curse of dimensionality effects. The paper proposes to overcome these limitations.