This article explores the assumption that a deeper (quantitative) understanding of the information-theoretic implications of sensory-motor coordination can help endow robots not only with better sensory morphologies, but also with better exploration strategies. Specifically, we investigate by means of statistical and information-theoretic measures, to what extent sensory-motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. The results show how the usage of correlation, entropy, and mutual information can be employed (a) to segment an observed behavior into distinct behavioral states, (b) to analyze the informational relationship between the different components of the sensory-motor apparatus, and (c) to identify patterns (or fingerprints) in the sensory-motor interaction between the agent and its local environment.