This paper describes a neural network approach to phrase/clause boundary detection and letter-to-phoneme conversion that depend largely on the target language specification. The phrase/clause boundary detection network is provided with the parts of speech of three consecutive words and determines whether there is a phrase/clause boundary between the first and second words. The letter-to-phoneme conversion network converts input letters to distinctive features of the phonemes. The performance evaluation for English shows that the phrase/clause boundary detection network, trained with 500 sentences and tested with another 500 sentences, correctly detected the phrase/clause boundaries with 95% accuracy, and the letter-to-phoneme conversion network, trained with 1000 words and tested with another 1000 words, converted letters to phoneme correctly with 85% accuracy.