Abstract A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The decision boundary of a quantum-like mixture Gaussian corresponds as well to the separation of probabilities for the switching Kalman filter. The quantum-like mixture Gaussian can improve the classification accuracy in machine learning by indicating the uncertain points should not be assigned to any class. We will demonstrate the principle by he Gaussian mixture model for classification of the iris data set. The use of this data set in cluster analysis is not common, since the data set only contains two clusters with rather obvious separation.