A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a library of nano-materials. We discuss a modeling strategy that relates the outcome of an exposure escalation experiment to nano-particle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a dataset on 24 metal oxides nano-particles, characterized in relation to their electrical, crystal and dissolution properties. Copyright © 2013 John Wiley & Sons, Ltd.