Outlier detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as one-class classification, in which a model is constructed to describe normal training data. In wireless sensor networks (WSNs), the outlier detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. For this case, the task amounts to create a useful model based on kernel principal component analysis (KPCA) to recognize data as normal or outliers. Recently, KPCA has used for nonlinear case which can extract higher order statistics. KPCA mapping the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based methods have been extensively investigated, and it have showed excellent performance. Within this setting, we propose KPCA-based Mahalanobis kernel as a new outlier detection method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA-based Mahalanobis kernel on real-word data obtained from Intel Berkeley are reported showing that the proposed method performs better in finding outliers in WSNs when compared with the original reconstruction error-based variant and the one-class support vector machine detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis kernel.