Determining the meaningful texture features for human embryonic stem cells (hESC) is important in the development of online hESC classification system. This paper proposes the use of novel support vector machine with bio-inspired one-against-all (OAA) multi-class structural and statistical Gabor descriptors for hESC classification. It investigates the statistical histogram information at four different orientations and two different window sizes of the Gabor filter. It demonstrates that statistical Gabor features are more accurate and reliable than a conventional histogram based features.