<p> This Master Thesis presents a new method of analyzing and classifying liquid crystal textures, using feed-forward neural networks and different clustering techniques. Liquid crystal phases are generally identified by human experts by polarizing optical microscopy observations of textures, based on typical defects, the smoothness or sharpness of domains and the birefringence colors of the films. The thesis aims to establish a novel algorithmic technique for liquid crystal texture analysis and classification. Using image analyzing software, a characterization vector with 22 parameters is extracted for each texture. Advanced clustering algorithms are used to classify textures based on those characteristic parameters. Furthermore, a ranking of the measurements is proposed to refine the accuracy of classification. The proposed methodology will lead to a reliable and simple technique for the physical investigation of liquid crystal materials. </p>