Nowadays, human identification is required for daily routine activities such as entering secured locations in addition to many other applications. To achieve biometric verification, higher security levels with easier user interaction is needed. Biometric verification help identifying people based on their extracted physical or behavioural features. These features should have certain properties such as uniqueness, permanence, acceptability, collectability, and affordable cost to employ any biometric system. Handwritten signatures are treated as the most natural method of verifying a person's identity as compared to other biometric and cryptographic forms of verification. The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures. This paper presents a method for verifying handwritten signatures by using a Neural Network architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. The normal Handwritten Signature Verification (HSV) uses image processing technique to verify the signatures. This takes much processing and time to differentiate the genuine and forged signatures. Keeping this in mind, we refurbished the existing system and made necessary improvements to modernize the processes. Such as through Deep Learning techniques, we can deal with signature verification more accurately as it has many algorithms to check the accuracy of the results.