An approach to developing damage prognosis (DP) solution that is being developed at Los Alamos National Laboratory (LANL) is summarized in this paper. This approach integrates aadvanced sensing technology, data interrogation procedures for state awareness, novel model validation and uncertainty quantification techniques, and reliability-based decision-making algorithms in an effort to transition the concept of damage prognosis to actual practice. In parallel with this development, experimental efforts are underway to deliver a proof-of-principle technology demonstration. This demonstration will assess impact damage and predict the subsequent fatigue damage accumulation in a composite plate. Although the project focus will be DP for composite materials, most of this technology can generalize to many other applications. The unique aspects of this approach discussed herein include: 1) multi-length scale damage models analyzed on tera-scale computer platforms that discretize composites on an individual lamina level, 2) integration of advanced sensors with Los Alamos's flight-hardened data acquisition system, 3) damage detection, based on a statistical pattern recognition approach, and 4) reliability-based metamodels with quantified uncertainty that can be deployed on microprocessors integrated with the sensing system for autonomous damage prognosis.