Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hierarchical annotation architecture, named as HANOLISTIC (Hierarchical Image Annotation System Using Holistic Approach), is defined with two layers. The first layer, called level-0 consists of annotators each of which is fed by a set of distinct descriptors, extracted from the whole image. This enables us to represent the image at each annotator by a different visual property of a descriptor. Since, we use the whole image, the problematic segmentation process is avoided. Training of each annotator is accomplished by a supervised learning paradigm, where each word is considered as a class label. Note that, this approach is slightly different then the classical training approaches, where each data has a unique label. In the proposed system, since each image has one or more annotating words, we assume that an image belongs to more than one class. The output of the level-0 annotators indicate the membership values of the words in the vocabulary, to belong an image. These membership values from each annotator is, then, aggregated at the second layer to obtain meta-level annotator. Finally, a set of words from the vocabulary is selected based on the ranking of the output of meta-level. The hierarchical annotation system proposed in this study outperforms state of the art annotation systems based on segmental and holistic approaches.