ELISPOT results used to be evaluated visually which, however, is inevitably subjective, inaccurate, and cumbersome. Even when applying automated image analysis to this end, the results are highly variable if the counting parameters are set subjectively. Since objective, accurate, and reproducible measurements are fundamental to science, major efforts have been undertaken over the last decade at CTL to understand the scientific principles behind ELISPOT data and to develop “intelligent” image analysis algorithms based on these principles. Thus, a spot recognition and gating algorithm was developed to automatically recognize the signatures of defined cell populations, such as T cells, discerning them from irrelevant cell types and noise. In this way, the science of ELISPOT data analysis has been introduced, permitting exact frequency measurement against background. As ELISPOT assays become a gold standard for monitoring antigen-specific T-cell immunity in clinical trials, the need has surfaced to make ELISPOT data transparent, reproducible, and tamper-proof, complying with Good Laboratory Practice (GLP) and Code for Federal Regulations (CFR) Part 11 guidelines. Flow cytometry-based and other immune monitoring assay platforms face the same challenge. In this chapter, we provide an overview of how CTL’s ImmunoSpot? platform for ELISPOT data analysis, management, and documentation meets these challenges.