This paper presents a method for the design of single Gabor filter for segmenting multi-textured images. The features are extracted by filtering with a linear filter and estimating the local energy of the filter response. Gabor filters have been applied successfully to the segmentation of textured images. Previous investigators have used bank of filters, where the filter parameters were predetermined and not optimized for particular task. A model of feature extraction process is required for the optimization, which is developed and assessed to get a single Gabor filter.The approach is assessed by supervised segmentation experiments and includes the design of Gabor filter, Gaussian filter, classifier and post processing. The classifier uses minimum distance algorithm and post processing uses morphological operators to remove spurious misclassifications. Results are presented that confirm the efficiency of the post processing method and support underlying mathematical models.