NOVELTY - The method involves receiving a training dataset (306) comprising a microscopic image (305) extracted from a physical object (303) and an associated class definition (304) for the microscopic image based on a product specification corresponding to the physical object. A set of machine learning classifiers is trained to construct a model to classify the microscopic image into classes (308) based on the training dataset. The microscopic image is received as test input to the classifiers to classify the microscopic image using the model into the classes based on the product specification.    USE - Method for classifying a microscopic image of paper, plastic, metal, alloy, leather, glass, wood, ceramic and clay for authenticating a physical object e.g. handbag, shoe, apparel, belt, watch, wine bottle, packaging, labels, accessory, jersey, sports apparel, golf club, cosmetics, medicine, pharmaceutical drug, electronic chip, circuit, phone, batteries, auto part, airline part, airbag, currency note, paper document, toy or food product (all claimed), using machine learning from microscopic variations.    ADVANTAGE - The method enables generating a reduced size feature map by a process of sub-sampling to reduce dimensionality of feature maps along with improved robustness in terms of shifts, translations and variations against distortions while using cloud based authentication solution in a simple and cost-effective manner.    DETAILED DESCRIPTION - The model is a mathematical model (307). The machine learning classifier is an n-layer convolutional neural network based classifier e.g. 8-layer, 12-layer, 16-layer, 20-layer and 24-layer network. The product specification is selected form a group consisting of brand name, product line and details on label of physical object. An INDEPENDENT CLAIM is also included for a system for classifying a microscopic image.    DESCRIPTION OF DRAWING(S) - The drawing shows a flow diagram illustrating a method for training a machine learning system by extracting microscopic images from physical object and generating a mathematical model from the machine learning system.    Physical object (303)    Class definition (304)    Microscopic image (305)    Training dataset (306)    Mathematical model (307)    Classes (308)