Mosaic sensors have been advanced as superior sensors for observing moving targets in cluttered background, because they are potentially more sensitive than scanning sensors. Processing data from mosaic sensors, however, differs greatly from processing data from scanning sensors. Mosaic sensors produce much more data and the staring array introduces problems in rejecting background and assembling tracks. This paper surveys the mosaic data processing sequence showing how the nature of mosaic sensors introduces processing challenges in most of its stages. Also identified are design variables, such as detector size or integration time, that affect how well these challenges can be met. As an example of a design variable's influence on processing performance, the effect of blur circle size on the performance of a specific tracking and trajectory estimation algorithm is presented.