This paper presents a comparative analysis of linear genetic programming and artificial neural network methods to solve classification tasks. Usually classification tasks have data sets containing a large number of attributes and records, and more than two classes that will be processed using, for example, created classification rules. As a result, by using classical method to classify a large number of records, a high classification error value will be obtained. The artificial neural networks are often used to solve classification task, mostly obtaining good results. The linear genetic programming is a new direction of evolution algorithms that is not widely researched and its application areas are not well defined. However, some advantages of linear genetic programming are based on genetic operators whose structure does not require complicated calculations. During this work approximately 400 experiments were conducted with linear genetic programming and artificial neural network methods, using various data sets with different quantity of records, attributes and classes. Based on the results received, conclusions on possibilities of using the methods of linear genetic programming and artificial neural networks in classification problems were drawn, and suggestions for improving their performance were proposed.