Tone recognition is a critical component for speech recognition in a tone language. One of the main problems of tone recognition in continuous speech is that several interacting factors affect F0 realization of tones. In this paper, we focus on the coarticulatory, intonation, and stress effects. These effects are compensated by the tone information of neighboring syllables, the adjustment of F0 heights and the stress acoustic features, respectively. The experiments, which compare all tone features, were conducted by feedforward neural networks. The highest recognition rates are improved from 84.07% to 93.60% and 82.48% to 92.67% for Thai proper name and Thai animal story corpora, respectively.