The presence of underwater mines can have serious consequences for ships and submarines, impeding their ability to navigate the vast oceans and seas. This issue demands urgent attention, especially since many mines go undetected for decades. It's important to mention that the expenses associated with creating and placing a mine typically range from 0.5 to 10% of the expenses involved in removing it. Furthermore, the process of removing mines can take up to 200 times longer compared to the time it takes to lay them. To tackle this problem, a range of mine countermeasure (MCM) techniques have been developed, including mine-hunting. This technique involves using Sonar signals to identify mine-like objects (MLO) and employing various classification methods to differentiate them from benign objects. In this research, we will compare the efficacy of classical machine learning models and the binary ANN classifier, which uses an encoder for feature extraction.