  A central limit theorem is established for a sum of random variables
belonging to a sequence of random fields. The fields are assumed to have zero
mean conditional on the past history and to satisfy certain conditional
$\alpha$-mixing conditions in space or time. The limiting normal distribution
is obtained for increasing spatial domain or increasing length of the sequence.
The applicability of the theorem is demonstrated by examples regarding
estimating functions for a space-time point process and a space-time Markov
process.
