In passive sonar, underwater moving objects are identified by the acoustic sounds they transmit. The spectrum of these sounds show features about the mechanism of the sound source, these features are discrete frequencies on the spectrum and frequency lines on the spectrogram. Variability in the underwater environment produce discontinuous broken or unstable fluctuating frequency lines. In this paper, we propose an efficient algorithm that estimate continuities of the discontinuous frequency lines and extract presence of the unstable frequency lines using neural networks and represent the proposed algorithm shows good performance in estimation and extraction the unstable frequency lines through the experiments.