A method of configuring a device for detecting a situation from among a set of situations (S-1,..., S-N) in which it is possible to find a physical system observed by a least one sensor, comprising the following steps: - receiving 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 storage media of the detection device and relating to the determined situation, by prior initializing these parameters, then updating these initialized parameters, characterized in that this prior initialization 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 said determined probability distributions between the hidden states of the statistical model being used, - determining, 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 the parameters of the statistical model being used from the determined representative probability distributions, and in that the method further includes a configuration step for the detection device such that the statistical model being used includes the parameters determined by said prior initialization and then said update. - receiving 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 storage media of the detection device and relating to the determined situation, by prior initializing these parameters, then updating these initialized parameters, - 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 said determined probability distributions between the hidden states of the statistical model being used, - determining, 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 the parameters of the statistical model being used from the determined representative probability distributions,