1. A method of configuring a device ( 10 ) for detecting a situation from among a set of situations (S- 1 ,. .. , S-N) in which it is possible to find a physical system ( 40 ) observed by a least one sensor ( 18 ), comprising the following steps: receiving ( 102 ) a sequence of observation data of the physical system, called a training sequence (L- 1 ,. .. , L-N), provided by the sensor and corresponding to a determined situation of the physical system, determining, from the training sequence, the parameters of a statistical hidden Markov model (HMM- 1 ,. .. , HMM-N) recorded onto the detection device's storage media ( 20 ) and relating to the determined situation, by prior initializing ( 104 - 116 ) these parameters, then updating ( 118 ) these initialized parameters, characterized in that this prior initialization ( 104 - 116 ) comprises the following steps: with the statistical model being used having a given number of hidden states, determining ( 104 , 106 ) multiple probability distributions from the training sequence, by dividing the sequence into sub-sequences and assigning to each sub-sequence a probability distribution statistically modeling it, the number of determined probability distributions being greater than the number of hidden states in the statistical model being used, distributing ( 106 - 114 ) said determined probability distributions between the hidden states of the statistical model being used, determining ( 110 ), for each hidden state in the statistical model being used and, from the probability distributions assigned to said hidden state, a single probability distribution that is representative of said hidden state, and initializing ( 116 ) the parameters of the statistical model being used from the determined representative probability distributions, and in that the method also includes a configuration step ( 122 ) for the detection device such that the statistical model being used includes the parameters determined by said prior initialization ( 104 - 116 ) and then said update ( 118 ).