Corrugated board is an environment-friendly, commonly used packing material. Its basic structure consists of two liners and a flute between them. Mechanical properties and strength of corrugated board depend on constituent papers but also on its geometry. Which , however, can be distorted due to various factors related to its manufacture process or use. The greatest distortion occurs in the corrugated layer, which, due to crushing, significantly deteriorates functional properties of cardboard. In this work, two algorithms for automatic classification of corrugated board types based on images of deformed corrugated boards using artificial intelligence methods are presented. A prototype of corrugated board sample image acquisition device was designed and manufactured. It allowed to collect an extensive database of images with corrugated board cross-sections of various types. Based on this database, two approaches for processing and classifying them were developed. The first method is based on identification of geometric parameters of the corrugated board cross-section using a genetic algorithm. After this stage, a simple feedforward neural network was applied to classify the corrugated board type correctly. In the second approach, the use of a convolutional neural network for corrugated board cross-section classification was proposed. The results obtained using both methods were compared, and the influence of various imperfections in the corrugated board cross-section was examined.