Bacterial contamination of food products is a serious public health problem that creates high costs for the food-processing industry. Rapid detection of bacterial pathogens is the key to avoiding disease outbreaks and costly product recalls associated with food-borne pathogens. Automated identification of pathogens using scatter patterns of bacterial colonies is a promising technique that uses image processing and machine learning approaches to extract features from forward-scatter patterns produced by irradiating bacterial colonies with red laser light. The feature vector used for this approach can consist of hundreds of features, and a sufficiently large number of training images is required for accurate classification. As most feature extraction algorithms have high computational cost, the feature extraction step becomes the bottleneck in the whole processing pipeline. Computational grid technologies provide a promising and economical solution to this problem. In this work we report the implementation of the laser-scatter-analysis technique on a computational grid. A set of more than 2000 images was used for training of classifiers. The invariant form of Zernike moments up to order 20, radial Chebyshev moments, and Haralick features were extracted. Linear discriminant analysis and support vector machine classifiers were used for classification. We report speed-ups achieved and the scalability of this approach for large sets of images and for higher-order moments. Laser-scatter-analysis technique combined with computational grid technology offers a feasible and economic solution for rapid and accurate detection and classification of bacterial contamination.