In this paper, we present a complete inferencing framework based on L-fuzzy sets, comprising fuzzification, inferencing itself, and both linguistic and numeric defuzzification strategies. We present the algorithms for each step, and then present a range of worked examples to illustrate the methods. Finally, we compare the results with similar examples which carry out ‘standard’ Mandani-style inference. To the best of our knowledge, this is the first time that practical algorithms for complete L-fuzzy inference have been presented.