In this final work the proces of learning comprehensible trees by disregarding complex instances is described. The theme includes the implementation of an algorithm which takes into account the accuracy of classified instances the same way as comprehensible classification tree as possible. Comprehensibility is the most important advantage of the classification trees compared to most other classifiers, therefore it is essential for the development and implementation of the algorithm. The introduction describes the domain in which the decision trees and various algorithms are used to discover more correct, useful and understandable models from data and it is given the formal definition of what is data mining. Furthermore the main problem is described and that is, why build a new algorithm that takes into account the depth of the tree. A brief description of Machine learning (Data Mining) is presented, an algortihm C4.5 (J48) and it is given an example the use of that algorithm in Machine learning. Description of the algorithm, the program that is used to implement the algorithm and the software Eclipse are described in further sections. The proposed algorithm is presented in the section “Proposed algortihm” that is actually the main task of this final work. The main algorithm presents grading learned instances which rely on the depth d of the leaf in the tree which is necessary for the instances to be correctly classified in the classes which they belong to. To learn trees, we use the existing algorithm C4.5 on every random subset of attributes. Key words: