We conduct a comparative study of the performance of a variety of spatial filtering techniques for detecting small targets against infrared terrain backgrounds. We consider parametric and nonparametric filters as well as an example of a robust estimation technique as applied to a parametric filter. In addition, we consider the effects of a clustering algorithm on filtering performance. Some of the filtering algorithms that we consider are matched filters, Laplacian filters, quadratic filters and the Robinson filter. Several filter dimensions are considered as well as the effects of a guard band. These algorithms are tested against the Lincoln Laboratory Infrared Measurement Sensor database which is comprised of high resolution dual band (3.5 to 5.2 (mu) , and 8 to 12 (mu) ) data taken at a variety of sites under a broad spectrum of atmospheric conditions and times of day. Target insertion effects are modeled carefully, incorporating the straddling of targets across adjacent pixels as well as the effects of diffractive blurring. We find that many of the filters that we have tested show similar performance and all are clutter limited.