Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15 % of energy. In recent years, deep neural networks (deep NNs) have made remarkable progress in the domain of NILM. In this paper, we the performance and limitations of using Gaussian Processes for solving NILM. We choose GPs due to three main reasons: i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and GPs; iii) by appropriately designing the kernel we can incorporate domain expertise. We find that vanilla GPs are not well-suited for NILM.