This article presents a method for the classification of cracks in reinforced concrete structures using an artificial neural network classifiers ensemble. The proposed approach addresses common challenges in crack detection by utilizing unsupervised learning techniques, which eliminate the need for manual image annotation during the training process. The model architecture includes multiple classification stages, the number of which is determined empirically to optimize the precision of crack type recognition. One of the core strengths of this method lies in its use of a diverse ensemble of neural network classifiers. Each model contributes to the final decision through a weighted voting mechanism, resulting in a robust and highly accurate classification system. The methodology demonstrates improved diagnostic precision across various types of structural cracks. Experimental validation using the DeepCrack dataset confirms the effectiveness of the proposed approach. The ensemble model significantly outperforms individual classifiers, achieving high recognition accuracy. Moreover, the system’s flexibility allows it to be integrated into intelligent computer vision platforms for automated inspection and condition monitoring of infrastructure. Overall, the proposed method enhances the reliability and automation of structural assessment tasks and can be applied in a wide range of engineering and industrial applications related to visual crack analysis.