Character recognition is a well-known area because of its wide applications, including text-searchable documents, digital storage and support, and manual work automation. Recognizing characters is challenging, and, on top of that, writer’s writing style habits make handwritten character recognition even more challenging. This article addresses a set of features and classifiers that are less complex to implement and give significant recognition rates for Hindi language characters. The proposed work is based on two shape descriptors, such as (1) histogram of oriented gradients and (2) geometric moments. These descriptors, when used as features, reflect properties of characters that minimize intraclass variations and maximize interclass variations. The generated feature set is tested using two supervised classifiers, namely support vector machine (SVM) and multilayer perceptron (MLP). A thorough investigation into various evaluation parameters has been presented. Experimental results show that high recognition results are obtained when both shape descriptors are used in combination with an SVM classifier. This technique is evaluated on four publicly available databases, and it achieves a recognition rate of 96.8% on one of the datasets. A comparative analysis of the proposed method with relevant and recent works of this field proves its superiority. This work introduces a much less complex and promising approach toward isolated handwritten character recognition.