Nuclear entry and exit of the NF-kappaB family of dimeric transcription factors plays an essential role in regulating cellular responses to inflammatory stress. The dynamics of this nuclear translocation can vary significantly within a cell population and may dramatically change e.g. upon drug exposure. Furthermore, there is significant heterogeneity in individual cell response upon stress signaling. In order to systematically determine factors that define NF-kappaB translocation dynamics, high-throughput screens that enable the analysis of dynamic NF-kappaB responses in individual cells in real time are essential. Thus far, only NF-kappaB downstream signaling responses of whole cell populations at the transcriptional level are in high-throughput mode. In this study, we developed a fully automated image analysis method to determine the time-course of NF-kappaB translocation in individual cells, suitable for high-throughput screenings in the context of compound screening and functional genomics. Two novel segmentation methods were used for defining the individual nuclear and cytoplasmic regions: watershed masked clustering (WMC) and best-fit ellipse of Voronoi cell (BEVC). The dynamic NFkappaB oscillatory response at the single cell and population level was coupled to automated extraction of 26 analogue translocation parameters including number of peaks, time to reach each peak, and amplitude of each peak. Our automated image analysis method was validated through a series of statistical tests demonstrating computational efficient and accurate NF-kappaB translocation dynamics quantification of our algorithm. Both pharmacological inhibition of NF-kappaB and short interfering RNAs targeting the inhibitor of NFkappaB, IkappaBalpha, demonstrated the ability of our method to identify compounds and genetic players that interfere with the nuclear transition of NF-kappaB.