Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.