We propose a new method for (global) Hurst exponent determination based on wavelets. Using this method, we analyze synthetic data with predefined Hurst exponents, fracture surfaces and data from economy. The results are compared to those obtained with Fourier spectral analysis. When many samples are available, the wavelet and Fourier methods are comparable in accuracy. However, when one or only a few samples are available, the wavelet method outperforms the Fourier method by a large margin. * Ingve.Simonsen@phys.ntnu.no † Alex.Hansen@phys.ntnu.no ‡ Olav-Magnar. Nes@iku.sintef.no To our knowledge, two papers discuss wavelet based techniques in connection with Hurst exponent measurements [8,9]. In [8], the wavelet transform modulus maxima method is introduced, and in [9], wavelet packet analysis is used to extract the Hurst exponent. The method we present here differs from both of these. We would also like to mention two other papers which discuss self-affine fractals by using wavelets [10,11]. However, these two papers are mainly concerned about the so-called inverse fractal problem for self-affine fractals and not measuring of Hurst exponents. This paper is organized as follows. In Sec. II we review the central properties of self-affine surfaces, while in Sec. III the wavelet transform is reviewed. Sec. IV presents the derivation of the scaling relation for self-affine functions in the wavelet domain. In Sec. V this scaling relation is applied to synthetic and experimental data, and Hurst exponents are extracted. Finally, in Sec. VI, our conclusion is presented.