Separable filters, because they are specified separately in each dimension, require less memory space and present opportunities for faster computation. Mahalanobis and Kumar1 presented a method for deriving separable correlation filters, but the filters were required to satisfy a restrictive assumption, and were thus not fully optimized. In this work, we present a general procedure for deriving separable versions of any correlation filter, using singular value decomposition (SVD), and prove that this is optimal for separable filters based on the Maximum Average Correlation Height (MACH) criterion. Further, we show that additional separable components may be used to improve the performance of the filter, with only a linear increase in computational and memory space requirements. MSTAR data is used to demonstrate the effects on sharpness of correlation peaks and locational precision, as the number of separable components is varied.