Analysis of lace texture images is a challenging problem because the lace is a soft and extensible material and can be easily deformed. This paper investigates a whole system for lace classification. A first step, based on Otsu's segmentation method, allows to remove the background. Then the lace texture is characterized using local binary patterns (LBP). In order to be robust against rotation the Fourier Transform is applied on LBP histograms. The magnitude spectrum of this transform is then used as a feature vector. LBP descriptor parameters, including radius and number of neighbors, are adjusted in order to improve their relevance. The experiments show that the features based on LBP, with appropriate settings, produced good results in supervised and unsupervised contexts.