This chapter describes the application of machine learning techniques to solve biomedical problems in a variety of clinical domains. First, the concept of development and the main elements of a basic machine learning system for medical diagnostics are presented. This is followed by an introduction to the design of a diagnostic model for the identification of balance impairments in the elderly using human gait pattern, as well as a diagnostic model for predicating sleep apnoea syndrome from electrocardiogram recordings. Examples are presented using support vector machines (a machine learning technique) to build a reliable model that utilizes key indices of physiological measurements (gait/electrocardiography [ECG] signals). A number of recommendations have been proposed for choosing the right classifier model in designing a successful medical diagnostic system. The chapter concludes with a discussion of the importance of signal processing techniques and other future trends in enhancing the performance of a diagnostic system.