This paper looks at the nonparametric, multiscale, Markov Random Field (MRF) model and its application in classifying the MeasTex Test Suite. The MeasTex Test Suite is a standard by which various texture classification algorithms can be compared. Typically, today's texture classification algorithms have been based on supervised classification, whereby all the classification classes have been predefined. We look at a new texture classification scheme, one that does not require a complete set of predefined classes. Instead, our texture classification scheme is based on a significance test. A texture is classified based on whether or not its statistical properties can be deemed to be from the same population of statistics as that defines training set texture. If not, texture is deemed unknown. The advantages and disadvantages of such a scheme are discussed in this paper.