Texture analysis has been an area of active research for the last two decades. This paper presents results on the application of two techniques for the analysis of textured images. Specifically, we look at the two problems of identifying and classifying sandpaper samples based on the textural properties created by varying the sizing coat, and detecting and classifying wrinkles within the sandpaper sample. The techniques used for identifying the samples are based on mathematical morphology and the computation of neighboring grey level dependence matrices. The features generated by applying a four grey-level morphological operations to images with a 5 X 5 kernel are used to classify the different textures. The size and shape of the structuring elements, as well as the sequence of operations can be optimized to provide the best discrimination for a particular product. Next, we show that the techniques used for the solution of sizing coat problems have a natural extension to the wrinkle detection problem. The wrinkle detection problem may be thought of as detecting a time-varying signal in a noisy background when processing in the spectral domain. The method that we use is the modified Hough transform. The parameters obtained from this transform, the length, angle, and orientation of the wrinkle, are the parameters of interest to the manufacturer. This paper presents a description of the image processing system used, the algorithms employed for both the sizing coat height measurements and for wrinkle detection, and results obtained for actual data. With over 100 sandpaper samples, the algorithms have proven to be entirely successful, and the system is currently being implemented online in the sponsor's manufacturing facility.