Because conventional Boolean retrieval of soil survey data and logical models for assessing land suitability treat both spatial units and attribute value ranges as exactly specifiable quantities, they ignore the continuous nature of soil and landscape variation and uncertainties in measurement which can result in the misclassification of sites that just fail to match strictly defined requirements. This paper uses fuzzy classification to determine land suitability from (i) multivariate point observations of soil attributes, (ii) topographically controlled site drainage conditions, and (iii) minimum contiguous areas, and compares the results obtained with conventional Boolean methods. The methods are illustrated using data from the Alberta Agricultural Department experimental farm at Lacombe in Alberta, Canada. Data on site elevation and soil chemical and physical properties measured at 154 soil profiles were interpolated by ordinary block kriging to 15 m x 15 m cells on a 50 x 90 grid. The soil property data for each cell were classified by Boolean and fuzzy methods. The digital elevation model created by interpolating the elevation data was used to determine the surface drainage network and map it in terms of the numbers of cells draining through each cell on the grid. This map was reclassified to yield Boolean and fuzzy maps of surface wetness which were then intersected with the soil profile classes. The resulting classification maps were examined for contiguity to locate areas where a block of minimum size (45 m x 45 m) could be located successfully. In this study Boolean methods reject larger numbers of cells than fuzzy classification, and select cells that are insufficiently contiguous to meet the aims of the land classification. Fuzzy methods produce contiguous areas and reject less information at all stages of the analyses than Boolean methods. They are much better than Boolean methods for classification of continuous variation, such as the results of the drainage analysis.