Powered prosthetic legs are capable of improving the gait of lower limb amputees. Pattern recognition systems for these devices allow amputees to transition between different locomotion modes in a way that is seamless and transparent to the user. However, the potential of these systems is diminished because they require large amounts of training data that is burdensome to collect. To reduce the effort required to acquire these data, we developed an adaptive pattern recognition system that automatically learns from subject-specific data as the user is ambulating. We tested our proposed system with two able-bodied subjects ambulating with a powered knee and ankle prosthesis. Each subject initially ambulated with a pattern recognition system that was not trained with any data from that subject (making each subject a novel user). Initially, the pattern recognition system made frequent errors. With the adaptive algorithm, the error rate decreased over time as more subject-specific data were incorporated. When compared to a non-adaptive system, the adaptive system reduced the number of errors by 32.9% [8.6%], mean [standard deviation]. This study demonstrates the potential improvements of an adaptive pattern recognition system over non-adaptive systems presented in prior research.