This thesis proposes a vessel recognition and classification system based on acoustic signatures. Conventionally, acoustic sounds are recognized by sonar operators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with an automatic feature-based classification system. Therefore, it can be regarded reasonable to adopt the speech recognition algorithms in classification of underwater acoustic signal recognition (UASR). The most widely used feature extraction methods of speech recognition are Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) and they are also used in UASR. In addition, the Scattering transform is used to obtain filter bank instead of mel-scale filter bank in MFCC algorithm. The scattering cascade decomposes an input signal into its wavelet modulus coeffcients and various non-linearities are used between wavelet stages. The new proposed method is labeled as Scattering Transform Cepstral Coefficients (STCC). Sensitivity of human hearing system is not the same in all frequency bands and mel-scale filter bank in MFCC is more sensitive to small changes in low frequencies than high frequencies. Therefore, number of DWT decomposition levels is increased in low frequencies to determine accurate representation and experimental results shows that non-uniform filter banks provide better success rates. Non-linear Teager energy and hyperbolic tangent operators are used to increase the performance of classification in proposed features extraction methods. Non-linear operators and scattering transforms are used for the first time in UASR to identify the acoustic sounds of the platforms. Teager Energy Operator (TEO) estimates the true energy of the source of a resonance signal. TEO based MFCC, being more robust in noisy conditions than conventional MFCC, provides a better estimation of the platform energy. Although TEO has positive effect on MFCC, it decreases the performance of STCC. Di erent non-linear tanh operator is also applied to LPC, MFCC and STCC algorithms and experimental results show that tanh operator increases the performance of the classification in all feature extraction methods. This analysis and implementation was carried out with datasets of 24 different vessel signals recordings that belong to 10 separate classes of vessels. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are used as classifiers. Performance of the proposed methods is compared and experimental results demonstrate that STCC have the best performance and tanh based STCC achieves highest success rate with 98.50% accuracy in classification of vessel sounds.