Fluorescence lifetime imaging microscopy (FLIM) is one of the most sensitive techniques to measure metabolic activity in living cells, tissues and whole animals. We used two- and three-photon fluorescence excitation together with time-correlated single photon counting (TCSPC) to acquire FLIM signals from normal and prostate cancer cell lines. FLIM requires complex data fitting and analysis; we explored different ways to analyze the data to match diverse cellular morphologies. After non-linear least square fitting of the multi-photon TCSPC images by the SPCImage software (Becker & Hickl), all image data are exported and further processed in ImageJ. Photon images provide morphological, NAD(P)H signal-based autofluorescent features, for which regions of interest (ROIs) are created. Applying these ROIs to all image data parameters with a custom ImageJ macro, generates a discrete, ROI specific database. A custom Excel (Microsoft) macro further analyzes the data with charts and statistics. Applying this highly automated assay we compared normal and cancer prostate cell lines with respect to their glycolytic activity by analyzing the NAD(P)H-bound fraction (a2%), NADPH/NADH ratio and efficiency of energy transfer (E%) for Tryptophan (Trp). Our results show that this assay is able to differentiate the effects of glucose stimulation and Doxorubicin in these prostate cell lines by tracking the changes in a2% of NAD(P)H, NADPH/NADH ratio and the changes in Trp E%. The ability to isolate a large, ROI-based data set, reflecting the heterogeneous cellular environment and highlighting even subtle changes — rather than whole cell averages - makes this assay particularly valuable.