Russell Congalton literally wrote the book on the subject: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition (Mapping Science). I highly recommend reading it.
The most common way to assess the accuracy of a categorical land cover map is by using a confusion matrix (aka error matrix). The following shows the layout of a confusion matrix:
You can see that the validation classes, or "actual" values are along the x axis and the "predicted" classes, or your classified land cover map, are along the y axis. The matrix yields "user's accuracy", "producer's accuracy" and "overall accuracy". You can read more about those metric from Congalton (2008) or here. You can also calculate the KHAT statistic, which is the measure of agreement "...based on the difference between the actual agreement in the error matrix ... and the chance agreement that is indicated by the row and column totals..." (Congalton, 2008).
There are several options to actually put this into action. Erdas Imagine has an accuracy assessment tool. You can use a pivot table and formulas in Excel. Or, I prefer to do the accuracy assessments in R.
The following example from the caret package shows how the confusion matrix works. Of course, you would substitute your land cover classes for the iris species.
## 3 class example
newPrior <- c(.05, .8, .15)
names(newPrior) <- levels(iris$Species)
> confusionMatrix(iris$Species, sample(iris$Species))
Confusion Matrix and Statistics
Prediction setosa versicolor virginica
setosa 18 16 16
versicolor 17 18 15
virginica 15 16 19
Accuracy : 0.3667
95% CI : (0.2896, 0.4492)
No Information Rate : 0.3333
P-Value [Acc > NIR] : 0.2168
Kappa : 0.05
Mcnemar's Test P-Value : 0.9925
Statistics by Class:
Class: setosa Class: versicolor Class: virginica
Sensitivity 0.3600 0.3600 0.3800
Specificity 0.6800 0.6800 0.6900
Pos Pred Value 0.3600 0.3600 0.3800
Neg Pred Value 0.6800 0.6800 0.6900
Prevalence 0.3333 0.3333 0.3333
Detection Rate 0.1200 0.1200 0.1267
Detection Prevalence 0.3333 0.3333 0.3333
Balanced Accuracy 0.5200 0.5200 0.5350