Fuzzy logic inference systems (FISs) can help provide with-in field nitrogen (N) fertilization recommendations by combining critical plant- and soil-based spatial information. This chapter describes how, based on spatially distributed information, FIS can be used to develop in-season N recommendations. A sample problem is provided. Soil and plant information considered in this analysis included apparent soil electrical conductivity (ECa), elevation (ELE), and the remote sensing-based N Sufficiency Index (NSI = NDVIsample/NDVIwell fertilized reference). Expert knowledge for formulating fuzzy rules was developed from corn growth data following an in-season N application. The best mid-season growth response to in-season N occurred in areas of low ECa and high ELE. Under these favourable soil conditions, maximum mid-season growth was obtained without in-season N irrespective of the NSI values. Where soil conditions were less favourable (high ECa and low ELE), mid-season growth benefited from high in-season N rate only when NSI was low. These relations were modeled using a simple FIS having three inputs (ECa, ELE and NSI) fuzzified with only two sets (low and high), an output (optimum N rate) with three fuzzy sets (low, medium and high) and a set of eight simple rules. The FIS appeared to be a useful and handy tool for incorporating expert knowledge into spatially variable N recommendations. An example describing a basic implementation of the FIS in ArcGIS is included.