We present the first verification that a neural network for perception tasks produces
 a correct output within a specified tolerance for every input of interest. We define
 correctness relative to a specification which identifies 1) a state space consisting of
 all relevant states of the world and 2) an observation process that produces neural
 network inputs from the states of the world. Tiling the state and input spaces with
 a finite number of tiles, obtaining ground truth bounds from the state tiles and
 network output bounds from the input tiles, then comparing the ground truth and
 network output bounds delivers an upper bound on the network output error for
 any input of interest. Results from two case studies highlight the ability of our
 technique to deliver tight error bounds for all inputs of interest and show how the
 error bounds vary over the state and input spaces.
We present the first verification that a neural network for perception tasks produces a correct output within a specified tolerance for every input of interest. 