Abstract Learning and recognizing spatio-temporal patterns is an important problem for all biological systems. Gestures, movements and activities, all encompass both spatial and temporal information that is critical for implicit communication and learning. This paper presents a novel, unsupervised approach for learning, recognizing and early classifying spatiotemporal patterns using spiking neural networks for human robotic domains. The proposed spiking approach has four variations which have been validated on images of handwritten digits and human hand gestures and motions. The main contributions of this work are as follows: i) it requires a very small number of training examples, ii) it enables early recognition from only partial information of the pattern, iii) it learns patterns in an unsupervised manner, iv) it accepts variable sized input patterns, v) it is invariant to scale and translation, vi) it can recognize patterns in real-time and, vii) it is suitable for human-robot interaction applications and has been successfully tested on a PR2 robot. We also compared all variations of this approach with well-known supervised machine learning methods including support vector machines (SVM), regularized logistic regression (LR) and ensemble neural networks (ENN). Although our approach is unsupervised, it outperforms others and in some cases, provides comparable results with other methods