In this study, littoral wave conditions were transformed into image data and used to assess the applicability of the method to constructing a system for automatically digitizing and monitoring wave conditions. An image of ocean wave conditions was treated as a texture and its characteristics were examined as texture feature quantities representing the surface conditions in response to wind. These feature quantities were input to a hierarchical neural network for learning. The network, which had a multilayer structure adapted for the back-propagation algorithm, facilitated the study of the influence of learning conditions on the network structure. In addition, digital sensitivity analysis was performed to identify optimal calculation conditions for presenting an optimal image of the sea surface. Analyses were also performed using spatial color concentration dependence, with texture feature quantities consisting of energy, entropy, correlation, local uniformity, and inertia.