This paper addresses the problem of estimating the profile of a ship target from a sequence of forward-looking infrared (FLIR) imagery obtained at video rates. Signal processing takes place in two stages: first, a profile vector is extracted from each image frame by edge-detection processing; second, the resulting sequence of profile vectors is adaptively filtered in order to increase the signal-to-noise ratio. Several factors peculiar to FLIR data preclude the use of an otherwise straightforward temporal averaging approach. First, target resolution increases slowly over a sequence of image frames, gradually revealing details of the target profile. Second, over the course of a long sequence of frames a target may be affected by FLIR noise - random speckle, occlusions, and flaring - caused by various atmospheric and background phenomena. These factors must be met by a reliable, adaptive, nonlinear vector smoothing technique. In this paper we discuss techniques for simulating long sequences of realistic ship profiles, based on actual FLIR ship imagery. Profile extraction by hypothesis testing is also discussed. A number of adaptive and nonadaptive vector filtering techniques are considered. These are based on recursive filters, median filters, LMS filters, the shift-and-add method, segmenting algorithms, and combinations of these. We suggest operating constraints under which these algorithms are likely to be successful.