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Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. Various texture feature extraction methods include those based on gray-level values, transforms, auto correlation etc. We have chosen the Gray Level Co occurrence Matrix (GLCM) method for extraction of feature values. Image segmentation is another important problem and occurs frequently in many image processing applications. Although, a number of algorithms exist for this purpose, methods that use the Expectation-Maximization (EM) algorithm are gaining a growing interest. The main feature of this algorithm is that it is capable of estimating the parameters of mixture distribution. This paper presents a novel unsupervised segmentation method based on EM algorithm in which the analysis is applied on vector data rather than the gray level value. [ABSTRACT FROM AUTHOR]