A single outlier detection procedure for data generated from BL(1,1,1,1) models is developed. It is carried out in three stages. Firstly, the measure of impact of an IO, AO, TC and LC, denoted by IO  , AO  , TC  and LC  , respectively are derived based on least squares method. Secondly, test statistics and test criteria are defined for classifying an observation as an outlier of its respective type. Finally, a general single outlier detection procedure is presented to distinguish a particular type of outlier at a time point t.