This paper develops a new technique for modeling and classifying a growing texture using its evolution function over time. It encompasses morphological texture classification and parameter estimation with the objective of assessing the state of growth achieved by the texture using only a small sample set to train on, consistent with many real world situations for quality control. It is assumed that the texture model evolves over time according to the way in which its evolution function determines the parameters of its defining random process. This paper considers the random Boolean model for both binary and gray-scale images. A multiple linear regression model is used to estimate the Boolean model parameters as functions of the granulometric moments of the textures. Once the texture-model parameters are estimated, the time of the process can be found via the manner in which the parameters are determined by the dynamic evolutionary model.