This paper presents an evaluation of 5 letter-tosound (LTS) systems for Romanian. The first is an expert system; three of them use automatic classification methods with decision trees, neural networks and support vector machines respectively and the fifth one uses pronunciation by analogy. All systems were trained and tested on the same database: a 15,517 words corpus built according to the SpeechDat specifications and a corpus consisting of the most frequent 4779 words in Romanian. The results show that decision trees and neural networks generate the best results for letter to sound conversion in Romanian.