The problem of locating an object in noisy optical sensor data has arisen in many applications. The object location normally coincides with the center of a two-dimensional blur, when there is no noise. Thus one intuitively appealing estimate of the object location is the centroid of the area in which intensities of pixels exceed a certain threshold. Before the centroid is computed, the intensity data have usually undergone a series of signal processing steps. Errors are introduced in signal processing through sampling, non-uniform responses among scanning detectors, read-out noise, and quantization. In this paper, we evaluate the accuracy of using the centroid to estimate the object position. We report simulated accuracy as a function of design parameters such as sampling rate, noise variance, quantizer resolution, and signal-to-noise ratio. We also report the derivation of probability density function of the centroid assuming additive Gaussian white noise.