Physiological data often display fluctuations, which have been traditionally considered as noise. However, as Goldberger has emphasized, biological systems are deterministic systems with noise. This noise reflects inherent dynamics and is responsible for the adaptation of the organism to its surroundings. Various techniques derived from statistical physics have already been applied to biological signals, especially in the field of cardiovascular medicine, unravelling potential pathogenetic mechanisms of disease and leading to the construction of more accurate prediction models. Recently, considerable effort has been devoted by several research groups to the assessment of the inherent variability and complexity of the respiratory system, concerning both structure and function. A few clinical studies, mainly involving patients with asthma and chronic obstructive pulmonary disease (COPD), have demonstrated that identification of loss of complexity of respiratory signals can be of significant value in both diagnosis of disease and monitoring of therapy. This review presents results from these studies and describes the basic methods for the assessment of dynamics that govern respiratory physiology in health and disease.