Spelling recognition has been used for several purposes, such as enhancing speech recognition systems and implementing name retrieval systems. Tone information is an important clue, in addition to phones, for recognizing speeches in tonal languages. In this paper, we present a method to improve accuracy of spelling recognition in Thai, a tonal language, by incorporating tonerelated acoustic features to a well-known front-end feature named Perceptual Linear Prediction Coefficients (PLP). The proposed method makes use of three kinds of tone information: fundamental frequency (pitch), pitch delta and pitch acceleration, to enhance the original features. Compared to the baseline result gained from the original feature, our HMMs-based recognition model shows improvement of 1.73%, 2.85% and 3.16% of letter accuracy for close-type, mix-type and open-type language models, respectively.