The problem of estimating radar target parameters such as shape, size and material properties from monostatic scattering measurements is formulated within the context of statistical estimation theory and inverse scattering. A bayesian approach to this problem is proposed where the liklihood ratios between multiple target hypotheses are used to make an optimum target identification from sparse scattering data. A detailed analysis of this approach is presented using the polarization corrected physical optics approximation for cylindrically symmetric, conducting targets. It is shown that for such targets the statistical estimation problem becomes mathematically equivalent to the limited view problem to computed tomography when cast within a statistical framework. Extensions of the approach using alternative formulations of the inverse scattering problem are discussed.