Currency is the backbone of the Indian economy. Currency fraud is a serious offense that undermines a country’s financial stability. Because there are more counterfeit currencies on the market, India is dealing with a more serious issue. Currency identification has become a significant area of research nowadays. On November 8, 2016, the Indian government agencies announced the end of circulationof every single 500 and 1,000 rupee notes. There was also an introduction of ₹ 500 and ₹ 2,000 new currencies in exchange for demonetized currencies. After demonetization, there are many fake currencies came into the limelight. There are many fake currency detection approaches available as an alternative, but the majority of them are hardware-based and expensive. These security features are then encoded and fed into machine learning algorithms for feature detection and classification. A reliable approach for currency identification is formulated and also presents a framework for currency recognition based on the texture and color features of a currency, which applies to the Artificial Neural Network (ANN) model. The proposed work’s efficacy across various datasets is shown by experimental results, which also show that recognition performance can be achieved with a range of image sizes.