This paper proposes a novel technique for palmprint recognition in the transform domain and based on a combination of Principal Component Analysis (PCA) and Fourier transform. Although, PCA is widely adopted as one of the most promising tools for use in biometric recognition systems,it comes with its own limitations: poor discriminating power in the presence of variant illuminations, requires alarge computational load when the original dimensionality of data is high while the number of training samples is usuallylarge. Traditionally, to represent the palmprint image, PCA is carried out on the whole spatial image. In the proposed method, Fourier transform is used to decompose animage into its sine and cosine components, then the spectrumis used for PCA representation since doing PCA onthe whole frequency domain does not achieve any performance improvements. In comparison with the traditional PCA and three other methods, the proposed method yields better recognition accuracy and discriminating. In addition, the proposed method reduces the computational load significantly to half due to the symmetry property of Fourier transform.