This study constructs an automatic model for detecting and classifying asphalt pavement crack. Image processing techniques including steerable filters, projective integral of image, and an enhanced method for image thresholding are employed for feature extraction. Different scenarios of feature selection have been attempted to create data sets from digital images. These data sets are then employed to train and verify the performance of machine learning algorithms including the support vector machine (SVM), the artificial neural network (ANN), and the random forest (RF). The feature set that consists of the properties derived from the projective integral and the properties of crack objects can deliver the most desirable outcome. Experimental results supported by the Wilcoxon signed-rank test show that SVM has achieved the highest classification accuracy rate (87.50%), followed by ANN (84.25%), and RF (70%). Accordingly, the proposed automatic approach can be helpful to assist transportation agencies and inspectors in the task of pavement condition assessment.