Cerebral palsy (CP) is a non-progressive neuro-developmental condition that occurs in early childhood and is associated with a motor impairment, usually affecting mobility and posture. Automatic accurate identification of CP gait has many potential applications, for example, assistance in diagnosis, clinical decision-making and communication among the clinical professionals. In previous studies, support vector machine (SVM) and neural networks have been applied to classify CP gait patterns. However, one of the disadvantages of SVM and many neural network models is that given a gait sample, it only predicts a gait pattern class label without providing any estimate of the underlying probability, which is particularly important in computer aided diagnostics applications. The objective of this study is to first investigate different pattern classification paradigms in the automatic gait analysis and address the significance of Bayesian classifier model, and then give a comprehensive performances comparison. Bayesian classification is based on Bayes’ decision theory, which compute the probability of a given data point belonging to a class. Then among all classes, we choose the one that has the largest probability, and classify the data point as being of that class. Using a publicly available CP gait data set (68 normal healthy and 88 with spastic diplegia form of CP), different features including the two basic temporal–spatial gait parameters (stride length and cadence) have been experimented. Various hold-out and cross-validation testing show that the Bayesian model offers excellent classification performances compared with some popular classifiers such as random forest and multiple layer perceptron. With many advantages considered, Bayesian classifier model is very significant in establishing a clinical decision system for gait analysis.