Trajectory decoding from neural signals may be useful for the restoration of reach to paralyzed arms through functional electrical stimulation or the control of robotic arms or computer interfaces. Electromyograms (EMGs) are a popular non-invasive choice of signal source for neuroprosthetic interfaces but continuous trajectory control is challenging, especially when the set of muscles that can be recorded from is limited. One reason for this difficulty is that many applications provide only visual feedback to the users. In addition to motor impairments, spinal cord injury (SCI) may alter or eliminate sensation in the arm below the level of injury. We tested an EMG-controlled robot-assisted reaching task, in which the arm was moved in congruence with the output of the decoder, in 5 individuals with cervical SCI and 5 healthy controls. We also evaluated remote control of the robot, where the congruent sensory feedback at the arm was removed. We found a significant drop in performance without feedback at the arm that was larger for the individuals with SCI. Despite their sensory impairments, moving their arms as part of the task enabled functional control of reach that was impossible without the additional sensory information.