The gait cycle involves a repeated sequence of tasks that result in walking. The cycle is divided into two phases: the stance and swing phase. Both phases have further sub-phases. The stance phase may be considered from the perspective of the three functional foot-ankle fulcrums or ‘rockers’. Gait analysis is defined as the assessment of gait and identification of abnormalities. The different classes of gait data include temporal-spatial parameters, kinematics, and kinetics. Instrumented gait analysis uses motion capture systems and instrumented walkways to record gait data in these various classes. Evolving technologies for gait analysis include two–dimensional camera systems and wearable devices. Inertial measuring units are small electronic wearable devices containing sensors, which record temporal-spatial gait parameters. There is potential to use inertial measuring units in conjunction with machine learning to estimate more detailed gait data. The identification of abnormal gait patterns in patients with certain neurological disorders helps guide clinical decision-making. Pattern recognition and kinematic data have been used to develop a classification system for sagittal plane gait deviations in the spastic diplegic variant of cerebral palsy. Five common patterns are recognized. These are equinus, jump, apparent equinus, crouch and asymmetric gait patterns. Orthotics are important non-operative management devices. Surgery may be used to address specific primary or secondary pathology.