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Binary cloud masks generated from the manual interpretation of imagery serve as one source of truth data in the evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask (VCM) algorithm in the Suomi National Polar-orbiting Partnership (S-NPP) Calibration/Validation (CalVal) Program of the Joint Polar Satellite System (JPSS). The other and primary source of truth data used to establish global VCM performance comes from match-up datasets of VIIRS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) data that are collected by the NASA CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) system. While manually generated cloud masks have long been used to quantitatively evaluate the performance of individual cloud-screening detection tests, complete cloud-screening algorithms, and even the results of cloud forecast models, there has not previously been the opportunity to compare results obtained with these cloud masks to an independent, highly accurate cloud dataset, as collected by CALIOP. Such an opportunity was afforded the S-NPP VCM CalVal Team as it prepared to assess the VCM algorithm’s performance to meet the JPSS Validation Phase-1 performance criteria milestone. This article provides an in-depth discussion on the use of data generated to evaluate VCM performance and the results obtained from comparisons to both manually generated cloud masks and CALIOP-VIIRS match-up datasets. Overall, the performance of the VCM algorithm performance is found to be consistent with each source of truth data, and while the evaluation of the VCM algorithm is still in progress, it is already satisfying the JPSS Level-1 System Requirements. However, the similarities in VCM performance using these two sets of performance results were surprising and that becomes the focus of this article. As an example, the probability of correct typing (PCT) with the VCM algorithm during daytime conditions over ocean, land, and desert backgrounds, was 96.5%, 94.4%, and 95.7% respectively, based upon the manually generated cloud masks. Similar results obtained using CALIOP-VIIRS match-up datasets were 95.0%, 93.9%, and 96.0% respectively. It is concluded that manually generated cloud masks, created from VIIRS imagery, provide unique insights into the VCM’s performance, which results in a robust CalVal program when augmented with results obtained from CALIOP-VIIRS match-up data.