This thesis presents a process of a tree recognition by means of the computer vision, which analyses the tree bark. The procedure extracts LBP features from individual pictures of bark, which are used for training and testing by SVM. Since freely accessible collection of tree bark pictures does not exist, it was necessary to create a larger annotated collection which is also the first database publicly available. In recognition there is also a problem with scale or picture size, because different devices take pictures of different sizes, in different width/height proportions and mostly people do not take photographs from the same distance. The thesis also proposes a procedure that by means of the features gained by DoG detector, automatically determines the picture scale, by means of which the input picture is always rescaled in the reference size before the calculation of LBP. In the final experiment the 84.62 % accuracy was achieved on the collection of 12 trees.