Powered hip exoskeletons, in combination with passive prostheses, have been recently proposed to improve the economy and pattern of walking of lower-limb amputees within clinical scenarios. However, for everyday life support, a real-time control strategy that can accurately recognize different locomotion modes and transitions is required. In this letter, we proposed a novel locomotion recognition algorithm for an Active Pelvis Orthosis designed to assist people with lower-limb amputation, in quasi-static (sit-to-stand/stand-to-sit) and dynamic locomotion modes (walking and stairs negotiation). Two finite-state machines were combined to recognize in real-time the participants’ locomotion, one was a rule-based algorithm and one was based on four linear discriminant analysis classifiers. Four transfemoral amputees took part in the experiments and performed a circuit of tasks in two conditions, namely in transparent mode (the exoskeleton was controlled to provide null output impedance), and in assistive mode (the exoskeleton was controlled to output an assistive torque consistently with the locomotion mode recognized by the algorithm), to test the algorithm in real-time conditions. The median (25^th, 75^th percentile) between-subjects recognition accuracy was 94.8% (93.4%, 96.5%) with user-dependent models. Offline analysis on user-independent models with leave-one-subject-out validation resulted in between-subjects recognition accuracy equal to 95.9% (94.0%, 97.8%). The results of this study pave the way for future experimentations of the technology in ecological scenarios.