Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is implemented. Comprehensive texture database is employed to test its performance under different conditions. In the end, the system is applied to solve a real-world problem - the segmentation of aerial photos.