This paper addresses the problem of estimating the profile, or silhouette, of a ship target from a sequence of forward-looking infrared (FLIR) imagery obtained at video rates. We are specifically interested in the performance of multiframe processing techniques compared with single-frame methods. Our approach to multiframe signal processing combines spatial and temporal processing in two stages: first, a target profile is extracted from each image frame by a segmentation algorithm; second, the resulting sequence of profiles is temporally filtered in order to increase the signal-to-noise ratio. Long sequences of FLIR imagery tend to exhibit several characteristic forms of non-Gaussian noise (random speckle, occlusions, and flaring) caused by various atmospheric and background phenomena, as well as instrument noise. Simple temporal averaging is inadequate in this environment. In this paper we develop a combination of spatial and temporal processing algorithms that may be used to overcome the signal-to-noise ratio problems associated with these noise effects. A recursive temporal median filter in conjunction with a simple spatial segmentation algorithm is proposed for this purpose. Experimental results based on a database of FLIR ship imagery are presented in support of the spatiotemporal processor. Although the approach is developed for ship targets, we feel it could be adapted for a broader class of FLIR applications.