Patterned masks require requalification at wafer fabrication plants. Periodic inspections are done at the wafer fab to identify any new defects, such as haze and contamination, which develop or get added on the mask due to their usage and the way they are handled. These defects, if not monitored over time, may result in mask defects that print on the wafer. It is thus mandatory to identify and fix them in their early stages. Repeated inspections, coupled with higher sensitivity inspections employed at wafer fabs, result in a large number of defects reported, which includes many small and faint defects. However, some of these small and faint defects need detailed operator review time, due to their potential to grow and have a larger impact later. Efficiency of classification includes both speed and accuracy. For a manual review, accuracy is primarily affected by consistency, arising due to the bulk and monotonous nature of classification task affected by human fatigue. The requirement for higher accuracy however, necessitates increased operator review and analysis time. Increased operator review time translates to the amount of time a mask in not used for printing wafers, i.e. productivity loss. Calibre® DefectClassify™ tool enables automatic classification of defects by employing stable algorithms to ensure consistency and accuracy, while algorithm efficiency ensures adequate speed. The tool thus aids in improving the throughput and yield at wafer fabs. The tool reads defect images, analyzes image properties to extract potential defect regions, processes the regions to identify actual defects and classifies them. This paper mainly focuses on the challenges faced in characterization and classification of defects from images reported by the inspection machine. The primary difference between analyzing inspections at wafer fab and mask shop is the availability of layout data. Unavailability of layout data complicates the tasks of identifying different pattern regions on the mask, especially assist features. With advanced technology nodes, the number of assist features present is higher while the features themselves get smaller in size. These features, if not identified correctly, may be mistaken as defects. Other than that, single die defects are a category that gets affected due to lack of layout information. Without a reference to compare with, these defects require separate sets of rules to be applied to images for their identification and classification. In particular, identification of defects on pattern edges and corners from single die images is challenging without a reference image.