Apparatus for monitoring glucose in human or animal in real time, the apparatus comprising: a sensor providing a time series of measurements of glucose level, said measurements being indicative of an inferred level of said glucose in a part of said human or animal, wherein said inferred level comprises a level of said glucose in blood and said measurements comprise measurements of an interstitial glucose level and a processor which is adapted to perform the following steps: calculate a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory model, calculate a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory model with said second glucoregulatory model being a variation of said first glucoregulatory model and predict a combined estimate of the inferred glucose level based on a combination of the first and second estimates; wherein the first and second glucoregulatory models comprise a sub-model of glucose kinetics in the blood of said human or animal and a sub-model of interstitial glucose kinetics, wherein by using multiple models, an interacting multiple model strategy is being used, and wherein the processor is a state estimator and is adapted to define a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level. a sensor providing a time series of measurements of glucose level, said measurements being indicative of an inferred level of said glucose in a part of said human or animal, wherein said inferred level comprises a level of said glucose in blood and said measurements comprise measurements of an interstitial glucose level and a processor which is adapted to perform the following steps: calculate a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory model, calculate a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory model with said second glucoregulatory model being a variation of said first glucoregulatory model and predict a combined estimate of the inferred glucose level based on a combination of the first and second estimates; wherein the first and second glucoregulatory models comprise a sub-model of glucose kinetics in the blood of said human or animal and a sub-model of interstitial glucose kinetics, wherein by using multiple models, an interacting multiple model strategy is being used, and wherein the processor is a state estimator and is adapted to define a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level. calculate a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory model, calculate a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory model with said second glucoregulatory model being a variation of said first glucoregulatory model and predict a combined estimate of the inferred glucose level based on a combination of the first and second estimates; wherein the first and second glucoregulatory models comprise a sub-model of glucose kinetics in the blood of said human or animal and a sub-model of interstitial glucose kinetics, wherein by using multiple models, an interacting multiple model strategy is being used, and wherein the processor is a state estimator and is adapted to define a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level.