One approach to conveying tactile feedback from sensorized neural prostheses is to characterize the neural signals that would normally be produced in an intact limb and reproduce them through electrical stimulation of the residual peripheral nerves. Toward this end, we have developed a model that accurately replicates the neural activity evoked by any dynamic stimulus in the three types of mechanoreceptive afferents that innervate the glabrous skin of the hand. The model takes as input the position of the stimulus as a function of time, along with its first (velocity), second (acceleration), and third (jerk) derivatives. This input is filtered and passed through an integrate-and-fire mechanism to generate a train of spikes as output. The major conclusion of this study is that the timing of individual spikes evoked in mechanoreceptive fibers innervating the hand can be accurately predicted by this model. We discuss how this model can be integrated in a sensorized prosthesis and show that the activity in a population of simulated afferents conveys information about the location, timing, and magnitude of contact between the hand and an object.