In many image analysis and computer vision applications, object recognition is the ultimate goal. This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outline of the objects have been used for the whole process of the recognition. Hu?s moments and their extended counterparts have been used as features of the objects. Various similarity measures have been used and compared for recognition. The test objects are matched with the model objects in database and the object with the least similarity measure is taken as the recognized object. A detailed experimental study has been made under different conditions and circumstances including transformation, noise, and occlusion. Databases of different sizes have been used to have a look at various experimentations. Some interesting observations have been made which may be useful for research and practicing community.