In this paper, we introduce a new noise robust representation of speech signal obtained by locating points of potential importance in the spectrogram, and parameterizing the activity of time-frequency pattern around those points. These features are referred to as Spectro-Temporal Activity Pattern (STAP) features. The suitability of these features for noise robust speech recognition is examined for a particular parameterization scheme where spectral peaks are chosen as points of potential importance. The activity in the time-frequency patterns around these points are parameterized by measuring the dynamics of the patterns along both time and frequency axes. As the spectral peaks are considered to constitute an important and robust cue for speech recognition, this representation is expected to yield a robust performance. An interesting result of the study is that inspite of using a relatively less amount of information from the speech signal, STAP features are able to achieve a reasonable recognition performance in clean speech, when compared to the state-of-the-art features. In addition, STAP features produce a significantly better performance in high noise conditions. An entropy based combination technique in tandem frame-work to combine STAP features with standard features yields a system which is more robust in all conditions.