Signature verification systems are critical in keeping the security of real individuals in a number of sensitive applications (e.g. banking or legal papers) Based on the nature of signatures of human beings it is very difficult to deal with the variability of human signatures and the problems in recognizing genuine signatures from fake ones in traditional methods. This work examines the usage of Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs) for signature verification and investigates their capability in learning intricate patterns, allowing higher level classification. We propose a pioneering pipeline of MLP that works on static and dynamic signature data to provide feature extraction methods that accurately describe the signature features such as shape, velocity, pressure, timing in order to differentiate between genuine or fake signatures.MLP-ANN model is trained on the basis of an established signature sample set and subsequently verified using all the standard classification metrics like accuracy, precision, recall, and F1-score. The results show that MLP leads to much higher accuracy for signature verification than traditional machine learning efforts. Moreover, we elaborate about generalisation difficulty of neural model, dataset boundaries and preprocessing effects on performance. Using the results, in particular we point to the ability of MLP-ANNs to be applicable in simple ubiquitous verification systems on signatures but give directions for future work, e.g., integration with other deep learning models, as well adaptive to live/dead oriented systems.