Researchers have extensively applied Hidden Markov Model (HMM) to handwritten recognition in English, Chinese, and other languages. Most researchers have been using the left-right topology for handwritten and speech recognition. This research studied the effect of HMM topology on isolated on-line Thai handwritten recognition. The left-right, fully connected and the proposed topologies (left-right-left) were compared. The number of state of a character HMM for each topology was varied from 15 to 35 nodes and the one with the best training observations probability was selected. The feature used was Chain code-like with modification to represent originated quadrant position. The recognition results showed that the proposed topology increases the recognition rate in comparison to the most widely used left-right topology.