A circular paper chromatogram is obtained from an alkaline solution of silver nitrate and soil. The shape, size, color and textural patterns of the chromatogram image are hypothesized to contain important information of the mineral content in the soil. We present a method to automatically analyze the chromatogram image for feature extraction. Image pre-processing is an important step before extracting the features of the image. Chromatogram image preprocessing involves detecting the center of the chromatogram, normalization and then segmentation into different concentric regions. Since chromatogram patterns are similar to iris (human eye) patterns, we have adopted iris-preprocessing methods. In this paper, we present a combination of different approaches: to detect the center, normalize and segment the chromatogram. Centre detection algorithm finds the center of the chromatogram which is assumed as the origin for normalization. Chromatogram normalization involves transforming from Cartesian to polar coordinates, so that chromatogram looks like an unwrapped polar image. Finally, color texture segmentation is used to detect different regions. Results of feature extraction are compared to that given by soil experts to test the accuracy of the system.