This paper describes the algorithm and convergence properties of an adaptive echo canceller with linear predictor. Conventional echo cancellers based on the learning identification algorithm may not provide good performance, because the rate of convergence is low due to the high correlation of speech signals, and echoes at the beginning of calls cannot be cancelled. In order to obtain better convergence properties, the new echo canceller adopts a linear prediction as the method for decorrelating the speech signals. The identification of the echo path and the generation of the echo replica are conducted independently, and the identification of echo path is carried out with prediction errors of speech signals and echo signal when predictor coefficients are decided by the linear prediction of speech signals. The echo replica is generated by putting the received speech signal through the echo path model. Computer simulation has shown that the new echo canceller is converged faster than conventional echo cancellers and that the convergence properties are better as the degree of linear prediction is higher and the predictor coefficients are more accurate. In case the degree is five, the rate of convergence is about twice as high and Echo Return Loss Enhancement (ERLE) increases over 10 dB in comparison with the conventional one.