A multispectral classification algorithm is developed for classifying remotely-sensed data extracted from parcels in an agricultural region. The developed multispectral classification algorithm is based on the comparison of the probability density function of the mixture of three normal distributions constructed for a test parcel (test class) with the probability density functions of the mixture of three normal distributions constructed for control parcels (control or information classes) one by one according to the distances between them. A discriminant function is defined and a decision rule is established for the developed multispectral classification algorithm. The discriminant functions for the developed multispectral classification algorithm take values between 0 and 2, end points are included. The discriminant function values give extra information which can be used in decisions about the comparisons in the developed multispectral classification algorithm. The extra information includes similarity and difference percentages or degrees in the comparisons of a test parcel (test class) with control parcels (control or information classes). This makes the classification results more clear and could help researchers better interpret the classification results of the remotely-sensed data.