When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target(1,2). However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements(3,4). Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt＇s law(5). These profiles are robust to changes in the dynamics of the eye or arm, as found empirically(6,7) 7. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical ＇two-thirds power law＇(8,9). This theory provides a simple and powerful unifying perspective for both eye and arm movement control.