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Crack is a common form of pavement distress and it carries significant information on the condition of roads. The detection of cracks is essential to perform pavement maintenance and rehabilitation. Many of the highways agencies, in different countries, are still employing conventional, costly and very time consuming techniques which involve direct human intervention and assessment. Although automated recognition has been successfully performed for many pavement distresses, crack detection remains, to this date, a topic where reservations exist. A novel approach to automatically distinguish cracks in digital pavement images is proposed in this paper. The Gabor filter is proven to be a highly potential technique for multidirectional crack detection that was not done previously using the Gabor filter. Image analysis using the Gabor function is directly related to the mammalian visual perception, hence the choice of this method for crack detection. Results reported in this paper concentrate on pavement images with high levels of surface texture that makes crack detection difficult. An initial detection precision of up to 95% has been reported in this paper showing a good promise in the proposed method.