In this chapter, examples of identifying stochastic noisy signals are provided based on the materials from previous chapters. Experimental results of vibroacoustic signal identification for bearing assemblies are illustrated graphically with ellipses of dispersion characteristics of training sets of vibroacoustic signals in the coordinates of the Pearson curve system. Statistical estimations of identification characteristics for two models of electroencephalographic noisy signals are presented: autoregression with deterministic coefficients and autoregression with random coefficients, which are studied in biomedical technical systems. The identification of narrowband noisy signals is discussed using the method of envelope and phase, Hilbert transform, and a wide range of phase characteristics of signals. The provided examples of identifying stochastic noisy signals characterize their current period of usage. These examples do not cover all potential identification characteristics of noisy signals. The rapid development of information technologies and their application in creating modern hardware and software monitoring, identification, and diagnostics systems for technical systems, including energy systems, requires the development of advanced information support for their operation using potential identification characteristics of stochastic noisy signals.