Weather and life cycles, fuel markets, reliability rules, scheduled and random outages, renewables and demand response programs, all constitute pieces of the electricity market puzzle. In such a complex environment, forecasting electricity prices is a very challenging task; nonetheless, it is of paramount importance for market participants and system operators. Day-ahead price forecasting is performed in the present paper using a kernel-based method. This machine learning approach offers unique advantages over existing alternatives, especially in systematically exploiting the spatio-temporal nature of 10-cational marginal prices (LMPs), while nonlinear cause-effect relationships can be captured by carefully selected similarities. Beyond conventional time-series data, non-vectorial attributes (e.g., hour of the day, day of the week, balancing authority) are transparently utilized. The novel approach is tested on real data from the Midwest ISO (MISO) day-ahead electricity market over the summer of 2012, during which MISO's load peak record was observed. The resultant day-ahead LMP forecasts outperform price repetition and ordinary linear regression, thus offering a promising inference tool for the electricity market.