The performance of an inexpensive, inductive rule-building expert system, 1ST CLASS, using the ID3 algorithm was compared to that of SIMCA class modeling in classifying the binary mass spectra of 78 toxic and related compounds. The compressed mass spectra consisted of 17 masses chosen using information theory. The expert rules verified the six main classes and two subclasses found with SIMCA class modeling. These classes were: all benzenes and all alkanes/alkenes (alkaenes); nonhalobenzenes, chlorobenzenes, bromoalkaenes, and chloroalkaenes; and mono-, dichloroalkaenes and polychloroalkaenes. Training set classification accuracies obtained with the expert rules gave a classification accuracy of 97-100% vs. 79-96% for SIMCA. Predictive accuracy for the four main classes was 78%. In general fewer masses were involved with the rules than with the SIMCA models, and the rules are normally optimized with regard to minimum number of steps in the rule, not minimum number of variables. The expert rules work best with closed sets of objects where all possibilities can be included in the training sets. The expert rules can be taken to be specified paths along the perimeter of the multidimensional measurement space (hypercube) to a vertex nearest the SIMCA cylinders for an appropriate class. Overall the performance of the expert system was very good.