Texture recognition plays an important role in many computer vision tasks including segmentation, scene understanding and interpretation, medical imaging and object recognition. In some situations, the correct identification of particular textures is more important compared to others, for example recognition of enemy uniforms for automatic defense systems, or isolation of textures related to tumors in medical images. Such cost-sensitive texture classification is the focus of this paper, which we address by reformulating the classification problem as a cost minimisation problem. We do this by constructing a cost-sensitive classifier ensemble that is tuned using a genetic algorithm. Based on experimental results obtained on several Outex datasets with cost definitions, we show our approach to work well in comparison with canonical classification methods and the ensemble approach to lead to better performance compared to single predictors.