The aging of concrete infrastructure facilities is a global problem. External and internal harmful factors such as water ingress, freezing and thawing, fire, chemical attack and forces lead to concrete degradation, frequently progressing from its surface. It is important to know how deep the deterioration process is expanded from the surface into the depth of the concrete bulk and how much the concrete degraded. The purpose of the study was to demonstrate the feasibility of an approach based on the application of pattern recognition methods to the analysis of ultrasonic signals to assess two variable factors of interest - the material degradation degree and the thickness of the conditionally weak surface layer on the strong concrete underlay. A series of specimens was made according to the 2-factor grid experimental design in order to build a mathematical model for the evaluation. Ultrasonic signals were acquired in the surface transmission at 2 frequencies, 50 to 100 kHz by stepped profiling of the specimens’ surface. 2D spatiotemporal waveform matrices acquired by surface profiling, where the coordinates were the propagation distance and ultrasonic time, served as the material for processing. The approach based on the methods of pattern recognition included steps of transformation the signals by the discrete Fourier transform, calculation of statistical criteria and creation of decision rules as bilinear mathematical approximation surfaces of the criteria. Verification of the method by substituting data on a control series of specimens into the mathematical model built on the grid of specimens of a training series allowed a realistic determination of the both factors of interest for a specimen from the control series. However, the use of only formal statistical criteria was found not completely sufficient for reliable recognition in all control cases. Further improvement of recognition can apply additional decision rules based on wave propagation in the time domain and refining of the existing decision rules by implementation of weights of influence.