Anomaly detection in engineering systems is cast as a problem of detecting outliers to the distribution of observations representing a state of normality. We focus on anomaly detection in machine perception. We argue that in addition to outlier detection, anomaly detection in machine perception systems requires other detection mechanisms. They include incongruence detection, data quality assessment, decision confidence gauging, and model drift detection. These mechanisms are elaborated and their application illustrated on a problem of anomaly detection in a sports video interpretation system.