In recent years, the family of Burr XII distributions has been successfully and frequently used inmany applied areas. The object of this paper is to compere the maximum likelihood (ML) and the maximum product of spacing?; (MPS) estimation for this family. Since analytical approach is intractable, recourse is taken to extensive computer usage, in both Monte Carlo and bootstrap simulations. For sample sizes, it is shown that the MPS method of estimation is superior, in the senseof smaller mean squared errors (MSE), to the ML method for many parametric configurations. For large sample sizes (n≤30) MPS and ML methods give nearly identical results, as is to be expeded.