Person authentication based on only the name, password or person identification number is not secured enough. In recent years researchers have focused on human physiological and behavioral parameters, because these parameters are more unique and human-specific than traditional ones. This approach of person authentication is usually called biometric authentication. Signature is the most commonly used behavioral biometric which is investigated in two ways of online and offline by researchers. In online procedure, the temporal indices of signature such as signing velocity, and acceleration are involved to increase the accuracy relative to offline methods and to recognize counterfeit signatures. In this work, a unimodal authentication system based on the sounds of the signing of thirty persons is proposed. Signals are analyzed in online approach. Four different types of features based on cepstrum analysis and parametric models are extracted and classified with three different distance based classifiers. For the evaluation of the proposed authentication system, 10×10 fold cross-validation method is used and the results are reported in terms of False Accept Rate (FAR) and False Reject Rate (FRR) metrics.