In image classification, overinterpretation occurs when a classifier finds strong class-evidence in regions of an image that contain no semantically meaningful features. This issue can put into question the trustworthiness and reliability of image classification models. To diagnose the presence of overinterpretation, Sufficient Input Subset (SIS) procedures are used. These procedures can identify subsets of pixels within an image that are sufficient to maintain the model's initial prediction, and which constitute valid statistical signals that the model relies on. We focus on conducting a proper analysis of the SIS obtained through various SIS procedures to gain insight into the model's behavior.