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The ocean acoustic environment is astoundingly complex, consisting of numerous noise sources like ships, offshore oil rigs, marine life, shore waves and acoustic cavitations, featuring varying sound speed profiles, multi-path interferences, as well as other hydrodynamic phenomena. Irrespective of the type of the receiver system, whether active or passive, the signals picked up by the hydrophones are disturbed by these inherent anomalies of the propagating medium and poses a prime challenge to extract useful information from the chaotic mixtures of received signals. Blind Source Separation (BSS), an engineering paradigm which attempts to mimic the human cognitive capability of selectively extracting an interesting process amidst several similar competing processes, can be considered as a viable solution to the problem. In this paper, the effectiveness of Slow Feature Analysis (SFA) algorithm (Laurenz Wiskott et.al), a biologically motivated technique based on the concept of temporal slowness to extract invariant features from multivariate time series, for solving the problem of nonlinear BSS is investigated. A testing framework for underwater acoustic signal separation has been developed in Python with the aid of Modular toolkit for Data Processing (MDP), a stack of general purpose machine learning algorithms.