This paper explores the adaptation features of Multilayer Perceptron and LVQ3 under the speech phoneme recognition experiment. The result shows that Multilayer Perceptron can adapt to novel data easily but may lose the high performance on old data, and by contrast LVQ3 can maintain what it has learnt in the past but is hard to adjust itself to fit the novel data* if a relative great difference lies between the old and novel data. In order to gain a better adaptation algorithm, some modifications are added to LVQ3 and better results are achieved under the same experiment. A method to add in new code vectors for LVQ3 is also examined and a better result is also achieved. Finally, Some problems of the proposed algorithms are discussed.