Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate target subspaces. These radiance subspaces can then be used in an appropriate detection scheme to identify potential targets. The generation of these subspaces involves some a priori knowledge of data acquisition parameters, scene and atmospheric conditions, and possible calibration errors. Variation is allowed in the model since some parameters are difficult to know accurately. Each vector in the subspace is the result of a MODTRAN simulation coupled with a physical model. Generation of large target spaces can be computationally burdensome. This paper explores the use of statistical methods to describe such target signature spaces. The statistically modeled spaces can then be used to generate arbitrary radiance vectors to form a sub-space with potential "real-time" applications. Statistically modeled target subspaces, using limited training samples, were found to accurately resemble MODTRAN derived radiance vectors.