Development of a Character recognition system for Devnagari is difficult because (i) there are about 350 basic, modified (“matra”) and compound character shapes in the script and (ii) the characters in words are topologically connected. Here focus is on the recognition of offline handwritten Devnagari signatures that can be used in common applications like bank cheques, commercial forms, government records, bill processing systems, Postcode Recognition, Signature Verification, passport readers, offline document recognition generated by the expanding technological society. Challenges in handwritten signature recognition lie in the variation and distortion of handwritten signature or script since different people may use different style of handwriting, and direction to draw the same shape of any Devnagari character. This overview describes the nature of handwritten language, how it is translated into electronic data, and the basic concepts behind written language recognition algorithms. Handwritten Devnagari signatures are imprecise in nature as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth, unlikely the printed character. An approach using Artificial Neural Network is considered for recognition of Handwritten Devnagari Signature. The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using NN architecture. Various static (e.g., area covered, number of elements, height, slant, etc.) (Plamondon & Srihari, 2000, p. 63-84) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN (Daramola & Ibiyemi, 2010, p. 48-52). Several Network topologies are tested and their accuracy is compared. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature matching, feature comparison and classification) allowing us to gain a better control over the precision of different components. This paper focuses on classification, the last phase in the process, covering some of the most important general approaches in the field. Each approach is evaluated for applicability in signature verification, identifying their strength and weaknesses. It is shown, that some of these weak points are common between the different approaches and can partially be eliminated with our proposed solutions. To demonstrate this, several local features are introduced and compared using different classification approaches.