Clustering techniques are commonly performed to find homogeneous and distinguishable prototypes. However, a careful interpretation of these prototypes is the key to assist the expert to better organize this knowledge for decision making support. We use the annotated Traffic Lights Panel (aTPL), not only as a postprocessing tool to help understand clustering prototypes, but also to manage uncertainty related to variability which is inherent by itself within the prototypes. The aTPL handles this uncertainty by using the variation coefficients (VC) in the classes over two dimensions - tone and saturation. Two different aTLPs were obtained to a WWTP in Slovenia. Results suggested that aTPL could be seen as a useful tool with good levels of reliability when interpreting and managing uncertainty related to decision-making based on clustering prototypes.