Many composite correlation filter designs have been proposed for solving a wide variety of target detection and pattern recognition problems. Due to the large number of available designs, however, it is often unclear how to select the best design for a particular application. We present a theoretical survey and an empirical comparison of several popular composite correlation filter designs. Using a database of rotational target imagery, we show that some such filter designs appear to be better choices than others under computational and performance constraints. We compare filter performance in terms of noise tolerance, computational load, generalization ability, and distortion in order to provide a multifaceted examination of the characteristics of various filter designs.