diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 0841cbd69..5a7d2eb5e 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -321,6 +321,10 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
     if (is.null(evnames) || any(evnames == ""))
       stop("each element of the watchlist must have a name tag")
   }
+  # Handle multiple evaluation metrics given as a list
+  for (m in params$eval_metric) {
+    params <- c(params, list(eval_metric = m))
+  }
 
   # evaluation printing callback
   params <- c(params)
diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
index e052499dd..94cd1ded3 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -232,12 +232,20 @@ test_that("train and predict RF with softprob", {
 test_that("use of multiple eval metrics works", {
   expect_output(
     bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-                  eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
-                  eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
+                   eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
+                   eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
   , "train-error.*train-auc.*train-logloss")
   expect_false(is.null(bst$evaluation_log))
   expect_equal(dim(bst$evaluation_log), c(2, 4))
   expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
+  expect_output(
+    bst2 <- xgboost(data = train$data, label = train$label, max_depth = 2,
+                    eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
+                    eval_metric = list("error", "auc", "logloss"))
+  , "train-error.*train-auc.*train-logloss")
+  expect_false(is.null(bst2$evaluation_log))
+  expect_equal(dim(bst2$evaluation_log), c(2, 4))
+  expect_equal(colnames(bst2$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
 })