The development and evaluation of texture synthesis algorithms is discussed. We present texture synthesis algorithms based on the gray-level co-occurrence (GLC) model of a texture field. These algorithms use a texture similarity metric, which is shown to have high correlation with human perception of textures. Synthesis algorithms are evaluated using extensive experimental analysis. These experiments are designed to compare various iterative algorithms for synthesizing a random texture possessing a given set of second-order probabilities as characterized by a GLC model. Three texture test cases are selected to serve as the targets for the synthesis process in the experiments. The three texture test cases are selected so as to represent three different types of primitive texture: disordered, weakly ordered, and strongly ordered. For each experiment, we judge the relative quality of the algorithms by two criteria. First, we consider the quality of the final synthesized result in terms of the visual similarity to the target texture as well as a numerical measure of the error between the GLC models of the synthesized texture and the target texture. Second, we consider the relative computational efficiency of an algorithm, in terms of how quickly the algorithm converges to the final result. We conclude that a multiresolution version of the "spin flip'' algorithm, where an individual pixel's gray level is changed to the gray level that most reduces the weighted error between the images second order probabilities and the target probabilities, performs the best for all of the texture test cases considered. Finally, with the help of psychophysical experiments, we demonstrate that the results for the texture synthesis algorithms have high correlation with the texture similarities perceived by human observers.