We hypothesize that the central nervous system detects a loss of balance by comparing outputs predicted by a nominal, forward internal model with actual sensory outputs. When the resulting control error signal reaches an anomalously large value, this control error anomaly (CEA) signals a loss of balance and precedes any observable compensatory response. To test this hypothesis a multi-input, multi-output internal model of a standing forward reach task was developed which incorporated online model identification and a Gaussian failure detection algorithm. Eleven healthy young women were then asked to stand and reach forward to a target positioned from 95 to 125% of their maximum reach distance. Kinematic and kinetic data were recorded at 100 Hz unilaterally from the upper body, leg and foot. Evidence of successful CEA detection was a compensatory step between 100 ms and 2 s later. The results show that use of a threshold, set at three standard deviations from the mean, on error in the control of leg segment acceleration detected a CEA and correctly predicted a compensatory response in 92.6% of 108 trials. Leg acceleration control error was a better predictor than upper body or foot acceleration control error (P = 0.000). CEA detection performed more reliably than loss of balance detection algorithms based on kinematic thresholds (P = 0.000). The results support the hypothesis that a loss of balance may be identified via the use of a nominal forward internal model and probabilistic error monitoring.