The standard method for the analysis of body accelerations cannot accurately estimate the energy expenditure (EE) of uphill or downhill walking. The ability to recognize the grade of the walking surface will most likely improve upon the accuracy of the EE estimates for daily physical activities. This paper investigates the benefits of automatic gait analysis approaches including step-by-step gait segmentation and heel-strike recognition of the accelerometry signal in classifying various gradients. Triaxial accelerometry signals were collected from 12 subjects, performing walking on seven different gradient surfaces: 1) 92 m of 0(°) flat ground; 2) 85 m of ±2.70(°) inclined ramp; 3) 24 m of ±9.86(°) inclined ramp; and 4) 6-m pitch line of ±28.03(°) rake of stairway. Validity studies performed on a group of randomly selected healthy subjects showed high agreement scores between the automated heel-strike recognition markers, manual gait annotation markers, and video-based gait-segmentation markers. Thirteen subset features were found using a subset-selection search procedure from 57 extracted features which maximize the classification accuracy, performed with a Gaussian mixture model classifier, as estimated using sixfold cross-validation. An overall walking pattern-recognition accuracy of 82.46% was achieved on seven different inclined terrains using the 13 selected features. This system should, therefore, improve the accuracy of daily EE estimates with accurate measures on terrain inclinations.