In this paper, a Conic Section Function Neural Network (CSFNN) based system for signature recognition problem is developed. The purpose of this work is to optimize CSFNN parameters for signature recognition problem to be applied to the VLSI Neural Network (NN) chip. Signature database is constructed after some preprocessing techniques are applied on collected raw data. After the preprocessing phase, the database is introduced to the CSFNN. Then CSFNN parameters are optimized to obtain acceptable signature recognition accuracy for a compact NN chip. Simplicity of the CSFNN structure and the range of parameters make CSFNN suitable for hardware implementation for this problem.