Toe walking is commonly seen in children with neurological symptoms such as cerebral palsy. However idiopathic toe walking (ITW) in children is considered to be habitual. ITW children are categorized as toe walkers without any neurological problems, however they walk with their foot plantar-flexed. These children often suffer poor sport performance leading to low exercise levels and the associated consequences. If the condition is not treated, the ITW children eventually develop abnormal gait pattern as adults and could suffer from postural problems. However, ITW gait is difficult to observe since children can modify their gait when made aware of it. Gait analysis using heel accelerometry data in ITW children could provide an objective and quantitative description of their toe walking and may thus be beneficial for observing ITW. In this paper, we propose a technique based on Support Vector Machines (SVM) to recognize ITW gait patterns using heel accelerometry data. Test results indicated that the SVM is able to identify ITW gait patterns with a maximum accuracy of 87.5% when a feature selection algorithm was applied.