A neural network algorithm has been developed to identify in the statistical properties of coherent biological signals present in a mixture with other signals and interference. The algorithm includes real-time determination of the coherence function of signals between fluctuations in systemic blood pressure and blood flow velocities in the left and right middle cerebral arteries and the phase shift function between these signals in the Meyer wavelength range. To reduce the influence of noise, it is proposed to use the technique of a sliding frame, divided into windows. The coherence and phase shift functions obtained in the windows are averaged within the frame boundaries. As a result, smoothed functions can be obtained in the time-frequency domain. To detect infractions of the autoregulation process, it is proposed to use trained neural feedforward network, which generalizing property can be improved as new experimental data are obtained while maintaining a balance between individual and general characteristics of patients