One trend in modern object recognition from images is the use of multiple features and sensors which are combined for the object recognition task. To get better classification results the features used for the classification of the objects should be physically 'orthogonal'. To be independent of the kind of features and of their combination method, it is necessary to represent each feature in a unified measure. This measure should define the quality of the feature in the examined image. The measure must be unified, because only such a measure can be combined to a meaningful global result. This paper presents a method which normalizes different kinds of local features. A probabilistic approach is used which provides the unified measure. To map the feature information to a probabilistic interpretation, a generalized function model is used. It is largely independent of the type of application. Two examples of the presented method are shown. The first example uses the Chamfer-Distance to measure edge-features, the second one uses a gray-value correlation coefficient.