Hyperspectral imagery can provide very valuable information on land cover classes. However, it also presents many challenges in data analysis and interpretation as a result of the large amounts of data collected. For example, conventional methods for land use and land cover classifications may not be directly applicable. Such conventional methods typically require a preprocessing step to transform high dimensional data to a lower dimension, mostly by eliminating data redundancy. For decades, principal component analysis (PCA) has been widely used to decorrelate spectral bands for reducing dimensionality. It is a useful technique if the spectral class structure of the transformed data is distributed along the first few axes. Otherwise, the transformed data may be similar to the original data. In such cases, we have shown in an earlier work that the wavelet decomposition technique is a better approach. Wavelet decomposition can reduce hyperspectral data in the spectral domain for each pixel. By carefully combining PCA and wavelet techniques, we engender a new method that benefits from the strength of both techniques. The intent of the hybrid method is to provide a tradeoff between the accuracy and speed, as compared with PCA and wavelet methods. The effectiveness of this method is demonstrated by using hyperspectral data from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral instrument. The experimental results show that, for high reduction rates, the hybrid method is superior to pure PCA and to pure wavelet-based techniques.