The purpose of this paper is to describe some results of a study in which an analogue procedure developed in The Netherlands is used to formulate objective probabilistic temperature forecasts on an experimental basis. As currently employed, the procedure routinely provides forecasters at the Royal Netherlands Meteorological Institute with guidance information that summarizes, for days 11 through 6, the weather conditions associated with the best thirty analogues of the corresponding forecast situation. In the work reported here, the empirical frequency distribution of maximum temperature corresponding to these thirty analogues is used to generate both categorical and probabilistic forecasts of this element. Attention is focused on three types of probabilistic forecasts of maximum temperature: 1) a discrete distribution for five temperature classes 2) a variable-width credible interval., and 3) a fixed-width credible interval. Results of the experiment indicate that all three types of probabilistic temperature forecasts are quite reliable, in the sense that the forecast probabilities correspond closely to the relative frequencies of observed temperatures associated with these class/intervals. Moreover, the forecasts generally are more accurate and precise, according to several different measures of performance, than forecasts based on standards of reference such as climatology and persistence. Thus, these experimental objective probability forecasts usually exhibit positive skill. As expected, the level of skill decreases markedly from day 1 to day 6 for all three types of probability forecasts. Evaluation of two types of categorical forecasts—the median and mean temperatures derived from the empirical frequency distribution—reveals similar results. The implications of the results of this study for operational temperature forecasting are discussed briefly, and some possible refinements and/or improvements in objective probabilistic temperature forecasting are described.