This thesis investigates how texture classification can be used to automatically classify tree species from image of bark texture. The texture analysis methods evaluated in the thesis are, grey level co-occurrence matrix (GLCM), two different wavelet texture analysis methods and finally the scale-invariant feature transform. To evaluate the methods two classifiers, a linear support vector machine (SVM) and a kernel based import vector machine (IVM) was used. The tree species that were classified were Scotch Pine and Norwegian Spruce and the auxiliary class ground. Three experiments were conducted to test the methods. The experiments used subimages of bark extracted from terrestrial photogrammetry images. For each sub-image, the X,Y and Z coordinates were available. The first experiment compared the methods by classifying each sub-image individually based on image data alone. In the second experiment the spatial data was added. Additionally feature selection was performed in both experiments to determine the most discriminating features. In the final experiment individual trees were classified by clustering all data from each tree. For sub-image classification, the addition of spatial data increased the overall accuracy for the best method from 75.7% to 94.9% The best method was IVM on GLCM textural features. The most discriminating textural feature was homogeneity in the horizontal direction. The best methods to classify individual trees were SVM with GLCM with an overall accuracy of 88%. In summary, the methods was found to be promising for tree bark classification. However, the individual tree results were based on a low number of trees. To establish the methods’ true usefulness, testing on a larger number of trees is necessary.