This paper uses the AURORA2 task to investigate the performance of our proposed tree-structured piecewise linear transformation (PLT) noise adaptation. In our proposed method, an HMM that best matches the input speech is selected based on the likelihood maximization criterion by tracing a tree structured HMM space that is prepared in the training step, and the selected HMM is further adapted by linear transformation. Experimental results show that our method achieves a significant improvement for the AURORA2 database.