While T lymphocytes have been employed as a cancer immunotherapy, the development of effective and specific T-cell-based therapeutics remains challenging. A key obstacle is the difficulty in identifying T cells reactive to cancer-associated antigens. The objective of this research was to develop a versatile platform for single cell analysis and isolation that can be applied in immunology research and clinical therapy development. Methods: An automated microscopy and cell sorting system was developed to track the proliferative behavior of single-cell human primary CD4+ lymphocytes in response to stimulation using allogeneic lymphoblastoid feeder cells. Results: The system identified single human T lymphocytes with a sensitivity of 98% and specificity of 99% and possessed a cell collection efficiency of 86%. Time-lapse imaging simultaneously tracked 4,534 alloreactive T cells on a single array; 19% of the arrayed cells formed colonies of ≥2 cells. From the array, 130 clonal colonies were isolated and 7 grew to colony sizes of >10,000 cells, consistent with the known proliferative capacity of T cells in vitro and their tendency to become exhausted with prolonged stimulation. The isolated colonies underwent ELISA assay to detect interferon-γ secretion and Sanger sequencing to determine T cell receptor β sequences with a 100% success rate. Conclusion: The platform is capable of both identification and isolation of proliferative T cells in an automated manner. Significance: This novel technology enables the identification of TCR sequences based on T cell proliferation which is expected to speed the development of future cancer immunotherapies.