This concept paper describes a novel approach to view-independent human gait analysis. Previous work in computer vision-based gait analysis typically use silhouette images from one side-view camera as input to the recognition system. Our approach utilises multiple synchronised cameras placed around the subject. From extracted silhouette images a 3D reconstruction is done that produces a segmented, labelled pose tree. Features are extracted from the pose tree and combined into an input case to the Case-Based Reasoning system. The input case is matched against stored past cases in a case base, and the most similar cases are classified by a Hidden Markov Model. The advantages of this approach are that subjects can be captured in a completely view-invariant way and recognition is efficient because unlikely candidates can be quickly discarded.