Falls are a large concern for individuals with lower limb amputations. Advanced powered prosthetic devices have the potential to quickly intervene after perturbations and help avoid a fall, but active balance recovery mechanisms have yet to be implemented. We investigated the feasibility of a realtime pattern recognition system for identification of trip recovery strategies. We tripped able-bodied subjects multiple times throughout swing phase and investigated the classification of walking, elevating and lowering strategies. Linear discriminant analysis was used throughout swing phase to classify kinematic data from the tripped leg. Window parameters that maximized classification accuracy were chosen from lengths of 50 to 200 ms and increments of 10 to 50 ms. We compared the performance of a single- and a two-stage (trip detection followed by strategy identification) classifier architecture. Optimal window length varied by classification stage, and window increment did not affect accuracy. The two-stage architecture performed significantly better overall, achieving a 92% median (range 88%-96%) accuracy across subjects compared to 88% (84%-96%) with the single-stage architecture. Most of the errors occurred immediately after the trip, with accuracies plateauing within 100 ms. Our results suggest that algorithms using data that can be measured from sensors embedded in robotic assistive devices could be used to trigger active balance restoring strategies following trips throughout swing phase.