Moment based invariants, in various forms, have been widely used over the years as features for recognition in many areas of image analysis. Typical examples include the use of moments for optical character recognition and shape identification. However, most of the work that has been carried out to date using moments and moment invariants is concerned with the identification of distinct shapes using binary images. There can be cases, though, where the different objects to be recognised share identical shapes and binary images fail to convey the necessary information to the recognition processes. The work presented in this paper not only looks at object recognition using binary images, but also addresses the issue of classification among objects which have identical shapes, using grey level images for the moment calculations. Two different moment based feature vectors that provide translation, scale, contrast and rotation invariance are used for the recognition of the different objects. These are the complex moments magnitudes and the Hu (1962) moment invariants. The performance of these two feature vectors are assessed both in the presence and absence of noise and the effect of extending the order of the moments used in their calculations is investigated.\u
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